Intra-day minimum new energy abandoned electric quantity determination method, system and device and medium

Abstract

The invention discloses an intra-day minimum new energy abandoned electric quantity determination method, system and device and a medium, and aims at solving the problem that the abandoned electric quantity and the limited load cost of new energy are minimum as target functions and solving the problem that the gradeability constraint, the spinning reserve constraint and the key section power constraint of a unit are considered. Meanwhile, the constraint of system inertia and the support capability constraint of the system on the normal operation of the large-scale power electronic equipment are considered, and finally, the established model is solved by utilizing a particle swarm algorithm. According to the method, the sufficient stable operation margin of the system can be kept under the condition that the electricity abandoning and load limiting cost of new energy is reduced as much as possible, and the method is easy to implement and better meets the operation requirements of the novel power system.

Description

Technical Field

[0001] The present application relates to the technical field of power system operation and dispatching, and in particular to a method, system, device and medium for determining the minimum amount of renewable energy abandoned power within a day. Background Art

[0002] For systems with a high proportion of new energy, since the output of new energy has random fluctuations and the accuracy of new energy output prediction is limited, the day-ahead unit scheduling strategy often has certain errors and needs to be corrected within the day.

[0003] The current daily modification strategy for scheduling arrangements is mainly to keep the startup mode unchanged, taking into account the new energy power abandonment and load limit strategies of unit climbing and standby constraints. However, the access of large-scale new energy with a high proportion has squeezed the available space of conventional units, resulting in reduced system strength, reduced the grid's support capacity for the normal operation of large-scale power electronic equipment such as new energy, and caused insufficient system inertia. These problems will threaten the safe and stable operation of the system.

[0004] Since the current intraday adjustment strategy does not take into account the grid’s support strength requirements and system inertia requirements for the normal operation of renewable energy, the current intraday correction strategy for scheduling arrangements cannot meet the actual system operation requirements. Summary of the invention

[0005] The present application provides a method, system, device and medium for determining the minimum amount of renewable energy power abandonment within a day, which is used to minimize the cost of renewable energy power abandonment and load limit while maintaining sufficient stable operation margin of the system. The method of the present application is simple to implement and better meets the requirements for the operation of new power systems.

[0006] In view of this, the first aspect of the present application provides a method, the method comprising:

[0007] Obtaining basic information of the power grid system and information of new energy sources connected to the power grid system;

[0008] Based on the basic information and the new energy information, taking the minimum amount of new energy abandonment and load limit as the objective function, and considering the inertia constraint of the power grid system and the support capacity constraint of the power grid system for the normal operation of power electronic equipment, a model for solving the minimum amount of new energy abandonment in the day of the new energy feed-in system considering the system safety constraint is established;

[0009] The particle swarm algorithm is used to solve the model for solving the minimum renewable energy power abandonment amount within the day, and the minimum renewable energy power abandonment amount and load limit are obtained.

[0010] Optionally, the process of constructing the support capacity constraint includes: utilizing an operating generalized short-circuit ratio indicator to consider the support strength constraint of the power grid system for new energy.

[0011] Optionally, the expression of the support strength constraint includes:

[0012] , t=1,...,T;

[0013] In the formula, is the matrix eigenvalue, T is the number of time periods considered, SB t is the capacity diagonal matrix composed of the output of renewable energy after abandoned power at each renewable energy feed-in point in the tth period, For SB t The inverse matrix, B m To obtain N through Schur complement operation w The power frequency node admittance matrix of the Thevenin equivalent network looking at the grid from the feed point, The larger the value, the better the given boot mode. The greater the support intensity of the power grid system for new energy in the t period.

[0014] SB t The expression is:

[0015]

[0016] Optionally, the expression of the brace strength constraint also includes:

[0017] ;

[0018] In the formula, It is the minimum generalized short-circuit ratio requirement of the power grid system set in advance.

[0019] Optionally, the expression of the inertia constraint is:

[0020]

[0021] In the formula, is the number of generators in operation, is the rated rotational kinetic energy of the i-th running generator, is the rated installed capacity of the i-th generator in operation, is the power forecast value of the i-th renewable energy station in the t-th period, is the amount of power abandoned by the i-th new energy station, is the amount of renewable energy connected to the grid system, is the rated frequency of the power grid system, T is the time period considered, is a pre-given limit on the frequency change rate.

[0022] Optionally, the objective function is expressed as:

[0023] ;

[0024] In the formula, T is the time period considered, A, C i are the load restriction cost coefficient and the power abandonment cost coefficient of the i-th new energy station, Δr t and ΔP i,t are the load limit in the tth period and the power abandonment of the i-th new energy station, is the amount of renewable energy connected to the power grid system.

[0025] Optionally, the constraints of the minimum renewable energy curtailment amount solution model also include: unit climbing capability constraints, spinning reserve constraints, and key section power constraints.

[0026] A second aspect of the present application provides a system for determining a minimum amount of renewable energy power wasted in a day, the system comprising:

[0027] An acquisition unit, used to acquire basic information of a power grid system and information of new energy sources connected to the power grid system;

[0028] A construction unit is used to establish a daily minimum renewable energy abandonment solution model for renewable energy feed-in systems taking into account system safety constraints based on the basic information and the renewable energy information, taking the minimization of renewable energy abandonment and load limit as the objective function, and taking into account the inertia constraint of the power grid system and the support capacity constraint of the power grid system for the normal operation of power electronic equipment;

[0029] The solving unit is used to solve the minimum renewable energy power abandonment solution model for the day by using a particle swarm algorithm to obtain the minimum renewable energy power abandonment and load limit.

[0030] The third aspect of the present application provides a device for determining the minimum amount of new energy abandoned in a day, the device comprising a processor and a memory:

[0031] The memory is used to store program codes and transmit the program codes to the processor;

[0032] The processor is used to execute the steps of the method for determining the minimum amount of renewable energy curtailment within a day as described in the first aspect according to the instructions in the program code.

[0033] A fourth aspect of the present application provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the method for determining the minimum amount of new energy curtailment within a day described in the first aspect.

[0034] It can be seen from the above technical solutions that this application has the following advantages:

[0035] The present application provides a method for determining the minimum amount of daily renewable energy curtailment in a large-scale renewable energy feed-in system that takes into account system safety constraints, with the objective function being to minimize the amount of renewable energy curtailment and load limit costs, while taking into account the unit's ramping capability constraints, rotating reserve constraints, and key section power constraints, as well as system inertia constraints and system support capabilities for the normal operation of large-scale power electronic equipment, and finally using a particle swarm algorithm to solve the established model. The method of the present application can minimize the cost of renewable energy curtailment and load limits while maintaining sufficient stable operating margin for the system. The method of the present application is simple to implement and better meets the requirements for the operation of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of a flow chart of a method for determining a minimum amount of renewable energy power abandonment within a day provided in an embodiment of the present application;

[0037] Figure 2 This is a structural schematic diagram of a system for determining the minimum amount of renewable energy power wasted in a day provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0039] See also Figure 1 A method for determining the minimum amount of renewable energy power wasted in a day provided in an embodiment of the present application includes:

[0040] Step 101: Obtain basic information of the power grid system and information of new energy sources connected to the power grid system.

[0041] It should be noted that, assuming that the new energy information connected to the system is as follows: There are N new energy sources w The rated installed capacity is: Pe1, Pe2, ..., Pe Nw Assume that the basic information of the power grid system is as follows: There are N generators in operation g The rated installed capacity is Sg k , rated rotational kinetic energy is W k , the output upper and lower limits are Pgmin k 、Pgmax k, the up and down climbing ability is Pg_up k 、Pg_down k ,k=1,..,N g Assuming that the structural parameters of the system are known (including grid structure parameters, new energy sources, generators, access points of loads, etc.), the needs of generalized short-circuit ratio calculation can be met.

[0042] Step 102: Based on the basic information and new energy information, taking the minimization of new energy abandonment and load limit as the objective function, and considering the inertia constraint of the power grid system and the support capacity constraint of the power grid system for the normal operation of power electronic equipment, a model for solving the minimum new energy abandonment in the day of the new energy feed-in system considering the system safety constraint is established.

[0043] In one embodiment, the process of constructing the support capacity constraint in step 102 includes: using a generalized short-circuit ratio index to consider the support strength constraint of the power grid system for new energy.

[0044] In one embodiment, the constraints of the minimum renewable energy curtailment amount solution model in step 102 also include: the ramp capability constraint of the unit, the spinning reserve constraint, and the critical section power constraint.

[0045] It should be noted that the model for solving the minimum amount of renewable energy curtailment in a large-scale renewable energy feed-in system considering system safety constraints is as follows:

[0046] Objective function and constraints:

[0047]

[0048] In the formula, T is the time period considered, A, C i are the load restriction cost coefficient and the power abandonment cost coefficient of the i-th new energy station, Δr t and ΔP i,t are the load limit in the tth period and the power abandonment of the i-th renewable energy station, P i,t is the power forecast value of the i-th renewable energy station in the t-th period, Pd t is the load in the tth period, SD t is the reserve requirement for the tth period, D i_j is the sensitivity coefficient of the output of the i-th machine at the j-th section, Pl j is the power constraint of the jth section, and L is the number of sections to be examined. (1)-(8) are linear programming problems, which can be solved by mature methods.

[0049] The following describes the specific model of the constraints on system inertia and the system's ability to support the normal operation of large-scale power electronic equipment.

[0050] The constraints considering the system inertia requirement are as follows:

[0051] (9)

[0052] Where f0 is the rated frequency of the system (such as 50Hz), is a pre-given limit on the frequency change rate.

[0053] The operating generalized short-circuit ratio indicator is used to consider the system's support strength constraints for large-scale renewable energy.

[0054] First, the equivalent admittance matrix is ​​obtained according to the given AC grid impedance parameters and topological structure, and then according to N w The number of new energy access points is obtained through the Schur complement operation. w The power frequency node admittance matrix B of the Thevenin equivalent network looking at the grid from a feed point m For each time period (t=1,2,...,T), the capacity diagonal matrix SB is composed of the output of the renewable energy after the abandoned power of each renewable energy feed-in point t .

[0055] SB t The expression is:

[0056]

[0057] The system's support strength for large-scale renewable energy can be measured by the generalized short-circuit ratio index, as shown below:

[0058] ...(11)

[0059] In the formula, is the matrix eigenvalue, The larger the value, the better the given boot mode. Under this condition, the grid system supports new energy more strongly in the t period. The following constraints are added:

[0060] (12)

[0061] In the formula, It is the minimum generalized short-circuit ratio requirement of the system set in advance.

[0062] According to the above method, a model (1)-(12) is established to solve the minimum amount of renewable energy curtailment in a large-scale renewable energy feed-in system considering system safety constraints.

[0063] Step 103: Use a particle swarm algorithm to solve the model for solving the minimum amount of renewable energy power abandonment within a day, and obtain the minimum amount of renewable energy power abandonment and load limit.

[0064] It should be noted that the following describes how to use the particle swarm algorithm to solve the problem.

[0065] Renewable energy curtailment and load limit Solve for the optimization variable, that is, for individual u i =(u 1_i ,...,u Nw×T+T_i ),u 1_i ~u Nw×T_i Represents 1~N in sequence w The amount of power abandoned by each new energy station in the 1st to Tth period, u Nw×T+1_i ~u Nw×T+T_i Represents the load limit of time periods 1 to T. i (n) is the local optimal individual of the ith particle after the nth iteration, and Uglobal(n) is the optimal individual of the entire particle swarm after the nth iteration. i (n) is the fitness value of the ith particle after the nth iteration, Flocal i (n) is the local optimal individual fitness value of the ith particle after the nth iteration, and Fglobal(n) is the global optimal individual fitness value after the nth iteration. The fitness function is shown as follows:

[0066] (13)

[0067] In the formula, when the individual does not meet the constraints, M takes a large positive real number (such as 1000000), otherwise it is 0.

[0068] Read in the parameters of the system to be studied. Assume that the population size is N p , randomly generate N p Individual u i =(u 1_i ,...,u Nw×T+T_i ), i=1,...,N p . Update each particle according to the following formula:

[0069]

[0070] In the formula, w max 、w min , c1, c2 are control parameters, n, N iter is the current iteration number and the total iteration number, and rand() is a random number between (0,1). i,j (n) represents the value of the jth position of the ith particle after the nth iteration, Ulocal i,j (n) is the value of the jth position of the local optimal individual of the ith particle after the nth iteration, Uglobal j(n) is the value of the jth position of the optimal individual of the entire particle swarm after the nth iteration. i,j (n) represents the velocity value of the i-th particle at the j-th position after the n-th iteration, , Respectively represent V i,j (n) has a lower and upper limit. best (n) represents the best particle (with the smallest fitness) in the particle swarm after the nth iteration.

[0071] Repeat the above iteration process until the termination condition is met. The optimal solution at this time is the required minimum renewable energy curtailment and load limit.

[0072] In the embodiment of the present application, a method for determining the minimum amount of new energy abandonment in a day for a large-scale new energy feed-in system considering system safety constraints is provided, with the abandonment amount of new energy and the minimum load limit cost as the objective function, while considering the climbing capability constraints, rotating reserve constraints, and key section power constraints of the unit, while considering the constraints of system inertia and the system's support capacity for the normal operation of large-scale power electronic equipment, and finally using a particle swarm algorithm to solve the established model. The method of the present application can minimize the abandonment of new energy and the cost of load limit, while maintaining sufficient stable operation margin of the system, and the method of the present application is simple to implement and better meets the requirements of the operation of new power systems.

[0073] The above is a method for determining the minimum amount of renewable energy abandoned power within a day provided in an embodiment of the present application, and the following is a system for determining the minimum amount of renewable energy abandoned power within a day provided in an embodiment of the present application.

[0074] See also Figure 2 , a system for determining the minimum amount of renewable energy power wasted in a day provided in an embodiment of the present application includes:

[0075] The acquisition unit 201 is used to acquire basic information of the power grid system and information of new energy sources connected to the power grid system.

[0076] The construction unit 202 is used to establish a daily minimum renewable energy abandonment amount solution model for the renewable energy feed-in system taking into account system safety constraints based on basic information and renewable energy information, taking the minimization of renewable energy abandonment amount and load limit as the objective function, and taking into account the inertia constraints of the power grid system and the supporting capacity constraints of the power grid system for the normal operation of power electronic equipment.

[0077] The solving unit 203 is used to solve the minimum renewable energy power abandonment solution model within a day by using a particle swarm algorithm to obtain the minimum renewable energy power abandonment and load limit.

[0078] Furthermore, an embodiment of the present application also provides a device for determining the minimum amount of renewable energy power abandoned within a day, the device comprising a processor and a memory:

[0079] The memory is used to store program code and transmit the program code to the processor;

[0080] The processor is used to execute the steps of the method for determining the minimum amount of renewable energy power abandoned within a day as described in the above method embodiment according to the instructions in the program code.

[0081] Furthermore, in an embodiment of the present application, a computer-readable storage medium is also provided, and the computer-readable storage medium is used to store program code, and the program code is used to execute the method for determining the minimum daily new energy power abandonment amount described in the above method embodiment.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0083] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0084] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0085] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0086] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0087] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0088] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0089] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining the minimum amount of renewable energy abandoned electricity within a day, characterized in that: include: Obtaining basic information of the power grid system and information of new energy sources connected to the power grid system; Based on the basic information and the new energy information, taking the minimum amount of new energy abandonment and load limit as the objective function, and considering the inertia constraint of the power grid system and the support capacity constraint of the power grid system for the normal operation of power electronic equipment, a model for solving the minimum amount of new energy abandonment in the day of the new energy feed-in system considering the system safety constraint is established; The particle swarm algorithm is used to solve the model for solving the minimum renewable energy power abandonment amount within the day, and the minimum renewable energy power abandonment amount and load limit are obtained.

2. The method for determining the minimum amount of renewable energy curtailment in a day according to claim 1, characterized in that: The process of constructing the support capacity constraint includes: using the operating generalized short-circuit ratio index to consider the support strength constraint of the power grid system for new energy.

3. The method for determining the minimum amount of renewable energy curtailment in a day according to claim 2, characterized in that: The expression of the support strength constraint includes: ,t=1,...,T In the formula, is the matrix eigenvalue, T is the number of time periods considered, SB t is the capacity diagonal matrix composed of the output of renewable energy after abandonment of power at each renewable energy feed-in point in the tth period, B m To obtain N through Schur complement operation w The power frequency node admittance matrix of the Thevenin equivalent network looking at the grid from the feed point, The larger the value, the better the given boot mode. The greater the support intensity of the power grid system for new energy in the t period.

4. The method for determining the minimum amount of renewable energy curtailment in a day according to claim 3, characterized in that: The expression of brace strength constraint also includes: ； In the formula, It is the minimum generalized short-circuit ratio requirement of the power grid system set in advance.

5. The method for determining the minimum amount of renewable energy curtailment in a day according to claim 1, characterized in that: The expression of the inertia constraint is: ； In the formula, is the number of generators in operation, is the rated rotational kinetic energy of the i-th running generator, is the rated installed capacity of the i-th generator in operation, is the power forecast value of the i-th renewable energy station in the t-th period, is the amount of power abandoned by the i-th new energy station, is the amount of renewable energy connected to the grid system, is the rated frequency of the power grid system, T is the time period considered, is a pre-given limit on the frequency change rate.

6. The method for determining the minimum amount of renewable energy curtailment in a day according to claim 1, characterized in that: The expression of the objective function is: ； In the formula, T is the time period considered, A, C i are the load restriction cost coefficient and the power abandonment cost coefficient of the i-th new energy station, Δr t and ΔP i,t are the load limit in the tth period and the power abandonment of the i-th new energy station, is the amount of new energy sources connected to the power grid system.

7. The method for determining the minimum amount of renewable energy curtailment in a day according to claim 1, characterized in that: The constraints of the minimum renewable energy curtailment amount solution model also include: unit climbing capability constraints, spinning reserve constraints, and key section power constraints.

8. A system for determining the minimum amount of renewable energy abandoned electricity within a day, characterized in that: include: An acquisition unit, used to acquire basic information of a power grid system and information of new energy sources connected to the power grid system; A construction unit is used to establish a daily minimum renewable energy abandonment solution model for renewable energy feed-in systems taking into account system safety constraints based on the basic information and the renewable energy information, taking the minimization of renewable energy abandonment and load limit as the objective function, and taking into account the inertia constraint of the power grid system and the support capacity constraint of the power grid system for the normal operation of power electronic equipment; The solving unit is used to solve the minimum renewable energy power abandonment solution model for the day by using a particle swarm algorithm to obtain the minimum renewable energy power abandonment and load limit.

9. A device for determining the minimum amount of renewable energy abandoned electricity within a day, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the method for determining the minimum amount of renewable energy power abandoned within a day as described in any one of claims 1 to 7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the method for determining the minimum daily renewable energy curtailment amount according to any one of claims 1 to 7.

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