Optical storage power station droop and inertia support coefficient feasible region evaluation method, medium and equipment

Abstract

The invention relates to the technical field of electric power system operation and control, in particular to an optical storage power station droop and inertia support coefficient feasible region evaluation method, a storage medium and equipment, and the method comprises the steps: employing operation data participating in a primary frequency modulation and inertia support process in a historical database of each optical storage power station in an electric power system; through state space mapping and least square data driving training, a linear function relation model between variables in the primary frequency modulation and inertia supporting process of each optical storage power station is constructed; and according to the model, optical storage power station-power system operator side online collaborative iteration is executed to evaluate the optical storage power station droop and inertia support coefficient feasible region. According to the method, a complex nonlinear physical model is converted into a linear model by adopting a data driving method, the solving speed is high, the method does not depend on model parameters, the frequency modulation capability of each optical storage power station can be fully utilized by adopting a collaborative iteration framework, and the serious calculation burden of system side centralized solving and the communication burden of traditional feasible region transmission are avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system operation and control, and particularly relates to a droop and inertia support coefficient feasible region evaluation method for a photovoltaic storage power station, a storage medium and equipment. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] The photovoltaic storage power station should evaluate the maximum droop-inertia support coefficient feasible region under different operating states and report it to the power system operator before participating in primary frequency regulation.

[0004] The droop and inertia control in the photovoltaic storage power station are both realized by adjusting the active power output, so they interact with each other. If the interaction mechanism between the droop and inertia support coefficients is ignored, it will be difficult for the photovoltaic storage power station to construct an accurate feasible region and realize the optimal PFR support.

[0005] In a power system with high renewable energy penetration, when a large number of photovoltaic storage power stations provide support according to the evaluation results, the frequency dynamics will change, resulting in different frequency changes under the same load fluctuation, which in turn affects the ability of each photovoltaic storage power station. In this case, in order to obtain the maximum coefficient boundary, it is very important to consider the coupling relationship of the primary frequency response ability between photovoltaic storage power stations. In the prior art, there is a method that uses the minimum frequency and the frequency change rate to construct the droop-inertia feasible region of each photovoltaic storage power station, and solves the optimal coefficient scheme according to the photovoltaic storage subarray rotor stability. However, the centralized evaluation in the above method requires the system operator to obtain the state measurement data of all photovoltaic storage subarrays in the grid, which will result in heavy communication burden, and it is difficult to upload real-time photovoltaic storage subarray state data from the photovoltaic storage power station.

[0006] On the contrary, the evaluation on the photovoltaic storage power station side shows significant features such as fast response, high reliability and light communication burden. However, the power system droop-inertia coefficient in some related literature is set as a constant, which cannot reflect the change of the system frequency regulation ability. In fact, the power system droop-inertia coefficient should be collected from multiple photovoltaic storage power stations in real time, rather than being set as a constant value. Therefore, it is very important to propose an iterative evaluation scheme that coordinates between the photovoltaic storage power station and the system operator, which should consider the change of the primary frequency response ability of the photovoltaic storage power station in the system.

[0007] Although the above coordination iteration methods do not require centralized modeling and evaluation, they still require the optical storage power station to obtain static state parameters. In this way, if the optical storage power station model is inaccurate or incomplete, the feasible region of the evaluation is certainly unreliable. In addition, long-time solving and optimization based on large-scale physical models make these methods unsuitable for online applications and tracking light changes. In order to cope with these problems, it is necessary to construct the optical storage power station model by using a suitable method.

[0008] In view of the above defects, the present application makes improvements. SUMMARY

[0009] In order to overcome the deficiencies of the background art, the present application provides a photovoltaic storage power station droop and inertia support coefficient feasible region evaluation method, a storage medium and equipment, which considers the mutual influence between the droop coefficient and the inertia support coefficient of the photovoltaic storage power station to construct the feasible region, adopts a data-driven method to convert the complex nonlinear physical model into a linear model, and the solving speed is fast and does not depend on the model parameters; The photovoltaic storage power station-power system operator side online collaborative iteration can fully utilize the frequency modulation capability of each photovoltaic storage power station, and avoids the serious calculation burden of centralized solving on the system side and the communication burden of traditional feasible region transmission.

[0010] The technical scheme adopted by the present application is: a photovoltaic storage power station droop and inertia support coefficient feasible region evaluation method, the method comprises: Using the operation data of each photovoltaic storage power station participating in the primary frequency modulation and inertia support process in the historical database of the power system, a linear function relationship model between each variable in the primary frequency modulation and inertia support process of each photovoltaic storage power station is constructed through state space mapping and least square data-driven training; According to the linear function relationship model, the photovoltaic storage power station-power system operator side online collaborative iteration is performed to evaluate the photovoltaic storage power station droop and inertia support coefficient feasible region.

[0011] Further, For the i-th photovoltaic storage power station, the variables in the primary frequency modulation and inertia support process of the photovoltaic storage power station include: power of the photovoltaic subarray primary frequency modulation start-up stage , final DC side voltage of the photovoltaic subarray , droop coefficient of the photovoltaic storage power station , inertia support coefficient of the photovoltaic storage power station , light , power system droop coefficient , and power system inertia coefficient .

[0012] Further, The historical database of each light storage power station in the power system is utilized to participate in the operation data of primary frequency modulation and inertia support process, and a linear function relationship model between each variable in the primary frequency modulation and inertia support process of each light storage power station is constructed through state space mapping and least square training, including: Each light storage power station in the power system obtains G data samples, and for the gth data sample of the ith light storage power station, the parameters included are: light storage subarray primary frequency modulation starting stage power , light storage subarray final DC side voltage , droop coefficient of light storage power station , inertia support coefficient of light storage power station , light , droop coefficient of power system , and inertia coefficient of power system ; According to the gth data sample, input samples and output samples are constructed, The input sample is defined as: (1) The output sample is defined as: (2) The input sample is dimensioned in state space to obtain the dimensioned input sample, which is defined as: (3) Wherein, represents the nonlinear enhanced observation dimension mapping vector function of the gth data sample of the ith light storage power station; Wherein, assuming that the number of enhanced observation dimensions is , then is defined as: (4) Wherein, is defined as: (5) Wherein, represents the e-th basis vector of the same dimension as , and represents the nonlinear enhanced observation dimension mapping scalar function of the e-th dimension; The output sample and the dimensioned input sample are arranged in order respectively to obtain the output sample set and the dimensioned input sample set, wherein the dimensioned input sample set of the ith light storage power station is defined as: (6) The output sample set of the ith light storage power station is defined as: (7) A linear function relationship model between variables in the primary frequency modulation and inertia support process of each optical storage power station is constructed through least square data-driven training, and a least square data-driven matrix of the i-th optical storage power station is The formula is as follows: (8) Wherein, represents the matrix transpose of represents the matrix pseudo-inverse of The linear function relationship model between variables in the primary frequency modulation and inertia support process of the i-th optical storage power station constructed is defined as: (9).

[0013] Further, The online collaborative iteration of the optical storage power station-power system operator side is performed according to the linear function relationship model to evaluate the droop and inertia support coefficient feasible region of the optical storage power station, and the method comprises the following steps: The real-time illumination of each optical storage power station is measured to update the illumination , and is substituted into the linear function relationship model of the corresponding optical storage power station; The iteration step is set; The power system droop coefficient and the power system inertia coefficient are calculated according to the following formulas (10)-(15) on the power system operator side, and are issued to each optical storage power station and substituted into the linear function relationship model of each optical storage power station; (10) (11) (12) (13) (14) (15) Wherein, represents the droop coefficient provided by the thermal power unit; On the side of each optical storage power station, the optical storage power station droop coefficient and the optical storage power station inertia support coefficient are solved according to the linear function relationship model and the issued power system droop coefficient and the power system inertia coefficient , and are reported to the power system operator; Judging power system droop coefficient and inertia coefficient whether the convergence condition in the following formula is met: (16) wherein, and is a set convergence condition; if the formula (16) condition is established, the droop coefficient feasible boundary of each optical storage power station and the inertia support coefficient feasible boundary are output; ; if the formula (16) condition is not established, the optical storage power station-power system operator side online collaborative iteration is continuously executed.

[0014] Further, at each optical storage power station side, according to the linear function relationship model and the issued power system droop coefficient and the power system inertia coefficient , the optical storage power station droop coefficient and the optical storage power station inertia support coefficient are solved and reported to the power system operator, including: setting iteration step ; setting the optical storage subarray final DC side voltage limit and the optical storage subarray active power limit during the primary frequency modulation process of the optical storage subarray, wherein, the optical storage subarray active power limit is defined as: (17) the optical storage subarray final DC side voltage limit is defined as: (18) wherein, is the first row in the least square data driven matrix , represents the second row in the least square data driven matrix , represents the i-th optical storage power station inertia support coefficient updated at the t+1 iteration, represents the i-th optical storage power station droop coefficient updated at the t+1 iteration; setting the droop coefficient initial value and the inertia support coefficient initial value of the optical storage power station in the online evaluation process of the optical storage power station side; solving the optical storage subarray active power limit equation by using the bisection method to obtain the iteratively updated inertia support coefficient feasible boundary ; Solving the final DC side voltage limit of the optical storage subarray by dichotomy Equation, get the iterative updated droop coefficient feasible boundary ; For each optical storage power station, judge the inertia support coefficient And the droop coefficient Whether the convergence condition in the following formula is met: (19) Wherein, And The set convergence condition; If formula (19) condition is established, output the droop coefficient And the inertia support coefficient , and report to the power system operator; If formula (19) condition is not established, let the iteration step Continue to iterate and execute the optical storage power station side evaluation.

[0015] Further, The set droop coefficient initial value and inertia support coefficient initial value of the optical storage power station in the online evaluation process of the optical storage power station side, comprising: Set the droop coefficient initial value of the optical storage power station ; Set the inertia support coefficient initial value of the optical storage power station .

[0016] Further, If formula (16) condition is not established, continue to execute the optical storage power station-power system operator side online collaborative iteration, comprising: If formula (16) condition is not established, let the iteration step Continue to execute the optical storage power station-power system operator side online collaborative iteration.

[0017] Further, The convergence condition And The value range is 10 -4 ~10 -3 .

[0018] Based on the same inventive concept, the application also provides a computer readable storage medium, which stores one or more programs, when the one or more programs are executed, the optical storage power station droop and inertia support coefficient feasible domain evaluation method described above can be realized.

[0019] Based on the same inventive concept, the application also provides an electronic device, comprising a processor, a communication interface, a computer readable storage medium as described above and a communication bus; wherein the processor, the communication interface and the computer readable storage medium communicate with each other through the communication bus; and the processor is used to execute the program stored in the computer readable storage medium.

[0020] Compared with the prior art, the application has the following beneficial effects: 1. The feasible region is constructed by considering the mutual influence between the droop coefficient and the inertia support coefficient of the optical storage power station, a data-driven method is used to convert the complex nonlinear physical model into a linear model, the solving speed is fast, and the model parameters are not dependent; 2. An evaluation framework of optical storage power station-power system operator side collaborative iteration is proposed, the frequency modulation capacity of each optical storage power station is fully utilized, and the serious calculation burden of system side centralized solving and the communication burden of traditional feasible region transmission are avoided; 3. A method of optical storage power station side and power system operator side alternating iteration is proposed to search and construct the feasible region of the optical storage power station, and the accurate analytical solution of the feasible region boundary can be realized.

[0021] Other features and advantages of the present application will be further described in the following specification, and some will become apparent from the specification, or will be understood through implementation of the present application. The purposes and other advantages of the present application can be realized and obtained through the structures indicated in the specification, claims and drawings.

[0022] The application will be further described below with reference to the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0024] Figure 1 The flowchart of the feasible region evaluation method of the droop and inertia support coefficients of the optical storage power station of an embodiment of the present application is shown in the figure. Figure 2 The structure diagram of the electronic device of an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0025] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0026] As shown in Figure 1 , the present application provides a method for evaluating the feasible region of droop and inertia support coefficients of a photovoltaic energy storage power station, which comprises the following steps: Using the operation data of each photovoltaic energy storage power station participating in primary frequency modulation and inertia support process in the historical database of the power system, a linear function relationship model between each variable in the primary frequency modulation and inertia support process of each photovoltaic energy storage power station is constructed through state space mapping and least square data driven training; According to the linear function relationship model, online collaborative iteration of photovoltaic energy storage power station-power system operator side is performed to evaluate the feasible region of droop and inertia support coefficients of the photovoltaic energy storage power station.

[0027] In the above technical solution, the linear function relationship model between each variable in the primary frequency modulation and inertia support process of each photovoltaic energy storage power station is well constructed by using relevant historical operation data and through state space mapping and least square data driven training. The feasible region of droop and inertia support coefficients of the photovoltaic energy storage power station can be well evaluated by performing online collaborative iteration of photovoltaic energy storage power station-power system operator side according to the constructed model. The above technical solution considers the mutual influence between the droop coefficient and the inertia support coefficient of the photovoltaic energy storage power station to construct the feasible region, adopts a data driven method to convert a complex nonlinear physical model into a linear model, simplifies the calculation, and has a fast solving speed, which can facilitate the implementation of high-precision coefficient feasible region evaluation without relying on model parameters. The online collaborative iteration of photovoltaic energy storage power station-power system operator side can fully utilize the frequency modulation capacity of each photovoltaic energy storage power station, and avoid the serious calculation burden of centralized solution of the system side and the communication burden of traditional feasible region transmission.

[0028] For the specific situation and characteristics of the photovoltaic energy storage power station, the input and output variables beneficial to the evaluation of the feasible region of droop and inertia support coefficients of the photovoltaic energy storage power station are selected. Specifically, taking the i th photovoltaic energy storage power station as an example, for the i th photovoltaic energy storage power station, each variable (i.e. input and output variables) in the primary frequency modulation and inertia support process of the photovoltaic energy storage power station includes: photovoltaic energy storage subarray primary frequency modulation startup stage power , photovoltaic energy storage subarray final DC side voltage , photovoltaic energy storage power station droop coefficient , photovoltaic energy storage power station inertia support coefficient , light , power system droop coefficient and the power system inertia coefficient The above variables include the droop coefficient and the inertia coefficient of the power system to which the optical storage power station is connected, that is, the dynamic characteristics of the power system are considered, and it is known that the embodiment of the application actually provides a feasible region evaluation method for the droop and inertia support coefficient of the optical storage power station considering the dynamic characteristics of the power system. The method can better evaluate the feasible region of the droop and inertia support coefficient of the optical storage power station by considering the dynamic characteristics of the power system, thereby better meeting the actual operation control requirements.

[0029] The optical storage power station droop and inertia support coefficient feasible region evaluation method using the above variables will be further described below.

[0030] The optical storage power station droop and inertia support coefficient feasible region evaluation method provided by the embodiment of the application comprises the following steps: Step 1, using the operation data of each optical storage power station participating in the primary frequency modulation and inertia support process in the historical database of the power system, state space mapping and least square training are used to obtain the state space mapping linear function relationship model (i.e. the aforementioned linear function relationship model) between the optical storage subarray primary frequency modulation starting stage power , the final DC side voltage of the optical storage subarray , the droop coefficient of the optical storage power station , the inertia support coefficient of the optical storage power station , the illumination , the droop coefficient of the power system and the power system inertia coefficient of each optical storage power station in the power system.

[0031] The above step 1 specifically comprises the following steps: Step 1-1, each optical storage power station in the power system obtains G data samples (G is not less than 1000 data samples in this embodiment), and the i-th optical storage power station is taken as an example. The parameters included in the g-th data sample of the i-th optical storage power station are: optical storage subarray primary frequency modulation starting stage power , the final DC side voltage of the optical storage subarray , the droop coefficient of the optical storage power station , the inertia support coefficient of the optical storage power station , the illumination , the droop coefficient of the power system and the power system inertia coefficient .

[0032] Step 1-2, taking the g-th data sample of the i-th optical storage power station as an example, input samples and output samples are constructed; The input sample is defined as: (1) The output sample is defined as: (2) Step 1-3, the state space is dimensioned, and the dimensioned input sample is obtained, which is defined as: (3) represents the nonlinear enhanced observation dimension mapping vector function of the gth data sample of the ith optical storage power station; the value of the function itself is also a column vector, and the e th element is a scalar function given by formula (5).

[0033] Where, assuming the number of enhanced observation dimensions is , then is defined as: (4) is defined as: (5) Where, represents the e th basis vector of the same dimension as , and represents the e th nonlinear enhanced observation dimension mapping scalar function. The parameter r i，g，e refers to the distance between the e th basis vector in the Euclidean space, and the symbol in formula (5) refers to the 2-norm. Here r i，g，e is only an intermediate variable, in order to facilitate the expression of the following scalar function.

[0034] The dimensioning process described in the above steps is the state space mapping; the relationship between the original input and output variables of the optical storage power station is very complex (i.e. the relationship between the original starting stage power, final speed and droop coefficient, inertia coefficient, light, etc. variable is very complex), here the role of state space mapping is to map the originally nonlinear complex variable relationship into a simple linear relationship in the mapped state space.

[0035] Step 1-4, respectively, the output sample and the dimensioned input sample are arranged in order to obtain the output sample set and the dimensioned input sample set, wherein the dimensioned input sample set of the ith optical storage power station is defined as: (6) The output sample set of the ith optical storage power station is defined as: (7) Step 1-5, through least square data driven training, the optical storage power station primary frequency modulation starting stage power optical storage sub-array final direct current side voltage optical storage power station droop coefficient inertia support coefficient light power system droop coefficient and power system inertia coefficient linear model (i.e. the aforementioned linear function relationship model) between the linear least squares data driven matrix of the ith optical storage power station The formula is as follows: (8) Wherein, represents matrix transpose, represents matrix pseudo-inverse; The linear function relationship model (i.e. the linear function relationship) between the variables in the primary frequency regulation and inertia support process of the ith optical storage power station is defined as: (9).

[0036] The above steps use least squares training, and the role of least squares training is to obtain the variable relationship in the mapped state space by using the least squares method through historical samples.

[0037] This embodiment considers the mutual influence between the droop coefficient and the inertia support coefficient of the optical storage power station to construct the feasible region, and uses a data-driven method to convert the complex nonlinear physical model into a linear model, which has fast solving speed and does not depend on model parameters.

[0038] Step 2, according to the linear function relationship (9) obtained in step 1, the optical storage power station-power system operator side online collaborative iteration evaluates the droop and inertia support coefficient feasible region of the optical storage power station.

[0039] For the above collaborative iteration evaluation, the current total frequency regulation and inertia support capability of the power system will affect the capability of each optical storage power station, therefore, the power system operator and the optical storage power station need to be iterated collaboratively, and each time the optical storage power station gets a new result, it reports to the power system operator, and the power system operator updates the total system capability, and then issues it to each optical storage power station, which re-evaluates, and finally converges.

[0040] This embodiment proposes an optical storage power station-power system operator side collaborative iteration evaluation framework, which fully utilizes the frequency regulation capability of each optical storage power station, and avoids the serious calculation burden of system side centralized solution and the communication burden of traditional feasible region transmission.

[0041] The specific execution process of step 2 is as follows: Step 2-1, measure the real-time light of each light storage power station to update the light , and substitute into the linear function relationship model of the corresponding light storage power station. The first half of the sentence here means that each light storage power station updates the light at this moment (i.e. when step 2-1 is executed) with the latest measured real-time light .

[0042] Step 2-2, set the iteration step .

[0043] Step 2-3, the power system operator side calculates the power system droop coefficient according to the following formulas (10)-(15) and the power system inertia coefficient , and issues and to each light storage power station, and substitutes into the linear function relationship model of each light storage power station; (10) (11) (12) (13) (14) (15) Wherein, represents the droop coefficient provided by the thermal power unit.

[0044] For the above formulas (10)-(15), formula (10) means that the total droop coefficient of the system is the sum of the droop coefficients of the thermal power (SG) plus the droop coefficients of each light storage power station; formula (11) means that the total inertia coefficient is the sum of the inertia coefficients of each light storage power station; formula (12) means that the initial droop coefficient of the system is the sum of the droop coefficients of the thermal power; formulas (13)-(15) mean that the initial droop and inertia of each light storage power station are 0, and the initial inertia coefficient of the system is 0.

[0045] Step 2-4, at each light storage power station side, according to the linear function relationship model and the issued power system droop coefficient and the power system inertia coefficient , solve the light storage power station droop coefficient and the light storage power station inertia support coefficient , and report to the power system operator; the specific execution process is as follows: Step 2-4-1, set the iteration step ; set the final DC side voltage limit of the light storage subarray allowed in the primary frequency modulation process of the light storage power station and the active power limit of the light storage subarray ,in, Optical storage array active power limit Defined as: (17) Final DC-side voltage limit of optical storage array Defined as: (18) in, Least squares data-driven linear matrix The first line in Represents a least-squares data-driven linear matrix The second line in This represents the inertia support coefficient of the i-th photovoltaic-storage power station updated in iteration t+1. This represents the droop coefficient of the i-th photovoltaic-storage power station updated in iteration t+1.

[0046] Step 2-4-2: Set initial values ​​for the sag and inertia support coefficients of the photovoltaic-storage power station during the online evaluation process, where the initial value of the sag coefficient of the photovoltaic-storage power station is... Initial value of inertia support coefficient for photovoltaic-storage power station .

[0047] Step 2-4-3: Solve for the active power limit of the optical storage array using the bisection method. The equation yields the iteratively updated feasible boundary for the inertia support coefficient. .

[0048] Step 2-4-4: Solve for the final DC-side voltage limit of the optical storage array using the bisection method. The equation yields the feasible boundary for iteratively updated droop coefficients. .

[0049] Steps 2-4-5: For each photovoltaic-storage power station, determine the inertia support coefficient. and droop coefficient Does it satisfy the convergence condition in the following formula: (19) in, and The convergence condition is set. If the condition in formula (19) is met, then the droop coefficient will be output. and inertia support coefficient And report it to the power system operator; If the above condition (19) is not met, then let the iteration step... Then proceed to step 2-4-3 to continue iterating and perform the photovoltaic-storage power station side assessment (i.e., perform the site side assessment).

[0050] Step 2-5, judging the droop coefficient of the power system and the inertia coefficient of the power system whether the convergence condition in the following formula is satisfied: (16) wherein, and is the set convergence condition; if the formula (16) condition is established, the droop coefficient feasible boundary of each optical storage power station and the inertia support coefficient feasible boundary are outputted; ; if the above condition (16) is not established, the iteration step is set to s, and the step 2-3 is returned to continue the optical storage power station-power system operator side online collaborative iteration. For the iteration step , here t is the support capacity of the power station under the current state of the system obtained by the iteration of the optical storage power station itself, and s is the iteration between the system and the optical storage power station; therefore, if the above convergence condition (16) is not established, the system and the optical storage power station should continue to iterate backward on the basis of the iteration result of the optical storage power station itself, and therefore the value of t+1 is assigned to s, and the step 2-3 is returned to redevelop the iteration process between the power system and the optical storage power station on the basis of the value.

[0051] The embodiment proposes a method of alternating iteration between the optical storage power station side and the power system operator side to search for the feasible region of the optical storage power station, and can realize the accurate analytical solution of the feasible region boundary.

[0052] The power system operator wants to know the primary frequency modulation and active support capacity of the optical storage power station, but if the calculation is performed at the system operator level, the operation data in each optical storage power station is difficult for the operator to obtain, and therefore it is difficult to accurately analyze. On the other hand, the total frequency modulation and inertia support capacity of the current power system will in turn affect the capacity of each optical storage power station, and therefore if the analysis and calculation are performed only at the optical storage power station side, the interaction between the support capacity of the optical storage power station and the total capacity of the system cannot be reflected. Therefore, the embodiment of the application proposes an evaluation framework of optical storage power station-power system operation side collaborative iteration, which solves the above problems through the collaborative iteration between the power system operator and the optical storage power station. After the optical storage power station obtains a new result each time, the system operator is reported, the system operator updates the total capacity of the system, and then the total capacity of the system is issued to each optical storage power station, and the latter reevaluates, and finally converges. The above process can truly realize the maximization of the support capacity.

[0053] According to a specific embodiment, the convergence condition and has a value range of 10 -4 ~10-3 The convergence condition of this embodiment and The preferred value range is 10. -4 ~10 -3 Convergence conditions and It remains unchanged during the iteration process; in specific implementation, the convergence condition... and Taking values ​​from the above range can achieve a better convergence effect, and thus can better evaluate the feasible region of sag and inertia support coefficient of photovoltaic-storage power station.

[0054] Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing one or more programs, which, when executed, can realize the aforementioned method for evaluating the feasible region of sag and inertia support coefficient of photovoltaic-storage power stations.

[0055] Based on the same inventive concept, the present invention also provides an electronic device, such as... Figure 2 As shown, it includes a processor, a communication interface, a computer-readable storage medium as described above, and a communication bus; wherein the processor, the communication interface, and the computer-readable storage medium communicate with each other via the communication bus; the processor is used to execute a program stored in the computer-readable storage medium.

[0056] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the present invention.

[0057] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not described in detail in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Parts not mentioned in the above embodiments are the same as or can be implemented using existing technology, and will not be further described here.

[0058] 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the feasible region of sag and inertia support coefficients in a photovoltaic-storage power station, characterized in that, The method includes: By utilizing the operational data from the historical databases of various photovoltaic and energy storage power stations within the power system that participated in the primary frequency regulation and inertia support processes, and through state-space mapping and least squares data-driven training, a linear function relationship model between variables in the primary frequency regulation and inertia support processes of each photovoltaic and energy storage power station is constructed. Based on the linear function relationship model, online collaborative iteration between the photovoltaic power station and the power system operator is performed to evaluate the feasible region of the sag and inertia support coefficient of the photovoltaic power station.

2. The feasible region evaluation method for sag and inertia support coefficient of a photovoltaic-storage power station according to claim 1, characterized in that, For the i-th photovoltaic-storage power station, the variables in the primary frequency regulation and inertia support process of the photovoltaic-storage power station include: Power during the first frequency modulation startup phase of the optical storage array The final DC-side voltage of the optical storage array droop coefficient of photovoltaic-storage power station Inertia support coefficient of photovoltaic-storage power station ,illumination Power system droop coefficient and power system inertia coefficient .

3. The feasible region evaluation method for sag and inertia support coefficient of a photovoltaic-storage power station according to claim 2, characterized in that, The method utilizes operational data from historical databases of various photovoltaic-storage power stations within the power system, covering the primary frequency regulation and inertia support processes. Through state-space mapping and least-squares training, a linear function relationship model is constructed between variables during the primary frequency regulation and inertia support processes of each photovoltaic-storage power station. This model includes: Each photovoltaic-storage power station in the power system acquires G data samples. For the g-th data sample of the i-th photovoltaic-storage power station, the parameters included are: power during the primary frequency regulation startup phase of the photovoltaic-storage subarray. The final DC-side voltage of the optical storage array droop coefficient of photovoltaic-storage power station Inertia support coefficient of photovoltaic-storage power station ,illumination Power system droop coefficient and power system inertia coefficient ; Construct input and output samples based on the g-th data sample. The input sample is defined as: （1） The output sample is defined as: （2） The input sample is then subjected to state space dimensionality upscaling to obtain the upscaled input sample, defined as: （3） in, The nonlinear enhanced observation dimension mapping vector function represents the g-th data sample of the i-th photovoltaic-storage power station; Where, it is assumed that the number of augmented observation dimensions is ,So Defined as: （4） in, Defined as: （5） in, Representative and The e-th basis vector of the same dimension The scalar function representing the e-th dimension of the nonlinear enhanced observation dimension mapping; Arrange the output samples and the upgraded input samples in order to obtain the output sample set and the upgraded input sample set, where the upgraded input sample set of the i-th photovoltaic-storage power station is defined as: （6） The output sample set of the i-th photovoltaic-storage power station is defined as: （7） By using least squares data-driven training, a linear function model of the relationships between variables during the primary frequency regulation and inertia support processes of each photovoltaic-storage power station is constructed. The least squares data-driven matrix for the i-th photovoltaic-storage power station is then established. The formula is as follows: （8） in, represent matrix transpose, represent The pseudo-inverse of the matrix; The linear function relationship model between the variables in the primary frequency regulation and inertia support process of the i-th photovoltaic-storage power station is defined as follows: （9）。 4. The feasible region evaluation method for sag and inertia support coefficient of a photovoltaic-storage power station according to claim 3, characterized in that, The step of performing online collaborative iteration between the photovoltaic power station and the power system operator to evaluate the feasible region of the sag and inertia support coefficient of the photovoltaic power station based on the linear function relationship model includes: Measure the real-time illumination of each photovoltaic-storage power station to update the illumination. And substitute it into the corresponding linear function relationship model of the photovoltaic energy storage power station; Set iteration steps ; The power system operator calculates the power system droop coefficient using the following formulas (10)-(15). and power system inertia coefficient and will and The data is distributed to each photovoltaic-storage power station and substituted into the linear function relationship model of each power station. (10) (11) (12) (13) (14) (15) in, This represents the sag coefficient provided by the thermal power unit. On each photovoltaic-storage power station side, based on the linear function relationship model and the issued power system droop coefficient... and power system inertia coefficient Solve for the droop coefficient of the photovoltaic-storage power station Inertia support coefficient of solar power storage station And report it to the power system operator; Determining the droop coefficient of a power system and inertia coefficient Does it satisfy the convergence condition in the following formula: （16） in, and The convergence condition is set. If the condition in formula (16) holds, then output the feasible boundary of the droop coefficient for each photovoltaic-storage power station. and inertia support coefficient feasible boundary ; If the condition in formula (16) is not met, the online collaborative iteration between the photovoltaic power station and the power system operator will continue.

5. The feasible region evaluation method for sag and inertia support coefficient of a photovoltaic-storage power station according to claim 4, characterized in that, At each photovoltaic-storage power station, based on the linear function relationship model and the issued power system droop coefficient... and power system inertia coefficient Solve for the droop coefficient of the photovoltaic-storage power station Inertia support coefficient of solar power storage station And report to the power system operator, including: Set iteration steps ; Set the allowable final DC-side voltage limit for the photovoltaic-storage subarray during the primary frequency regulation process of the photovoltaic-storage power station. and the active power limit of optical storage array ,in, Optical storage array active power limit Defined as: （17） Final DC-side voltage limit of optical storage array Defined as: （18） in, Least squares data-driven matrix The first line in Represents the least squares data-driven matrix The second line in This represents the inertia support coefficient of the i-th photovoltaic-storage power station updated in iteration t+1. This represents the droop coefficient of the i-th photovoltaic-storage power station updated in iteration t+1; Initial values ​​for the sag coefficient and inertia support coefficient of the photovoltaic-storage power station were set during the online evaluation process on the photovoltaic-storage power station side. Solving the active power limit of the optical storage array using the bisection method The equation yields the iteratively updated feasible boundary for the inertia support coefficient. ; Solving the final DC-side voltage limit of the optical storage array using the bisection method The equation yields the feasible boundary for iteratively updated droop coefficients. ; For each photovoltaic-storage power station, determine the inertia support coefficient. and droop coefficient Does it satisfy the convergence condition in the following formula: （19） in, and The convergence condition is set. If the condition in formula (19) is met, then the droop coefficient will be output. and inertia support coefficient And report it to the power system operator; If the condition in formula (19) is not met, then let the iteration step... Continue to iterate and perform evaluations on the photovoltaic-storage power station side.

6. The feasible region evaluation method for sag and inertia support coefficient of a photovoltaic-storage power station according to claim 5, characterized in that, The initial values ​​of the sag coefficient and inertia support coefficient of the photovoltaic-storage power station during the online evaluation process include: Set the initial value of the droop coefficient for the photovoltaic-storage power station ; Set the initial value of the inertia support coefficient for the photovoltaic-storage power station. .

7. The feasible region evaluation method for sag and inertia support coefficient of a photovoltaic-storage power station according to claim 5, characterized in that, If the condition in formula (16) is not met, the online collaborative iteration between the photovoltaic power station and the power system operator will continue, including: If the condition in formula (16) is not met, then let the iteration step... Continue to implement online collaborative iteration between photovoltaic power stations and power system operators.

8. The feasible region evaluation method for sag and inertia support coefficient of a photovoltaic-storage power station according to any one of claims 4-7, characterized in that, Convergence conditions and The value range is 10 -4 ~10 -3 .

9. A computer-readable storage medium storing one or more programs, characterized in that, When one or more of these programs are executed, the feasible domain evaluation method for sag and inertia support coefficient of photovoltaic-storage power stations as described in any one of claims 1-8 can be implemented.

10. An electronic device, comprising a processor, a communication interface, a computer-readable storage medium as described in claim 9, and a communication bus; wherein, The processor, communication interface, and computer-readable storage medium communicate with each other via a communication bus; Its features are, The processor is used to execute programs stored in a computer-readable storage medium.

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