Power grid new energy reserve capacity configuration method and system based on scene simulation

Abstract

The invention discloses a power grid new energy reserve capacity configuration method and system based on scene simulation, and the method comprises the steps: simulating the time correlation of a new energy prediction error along with the time change through employing a multi-scene technology based on a Markov chain, and generating a typical scene set of new energy output; establishing a power grid optimization scheduling model in the new energy output scene, and respectively calculating each typical power generation capacity in the typical scene set of the new energy output and the power generation capacity of the ground state scene of the new energy prediction curve; calculating the distance between the power generation capacity of each typical scene and the power generation capacity of the ground state scene, and obtaining a newly added standby demand corresponding to each typical scene; and carrying out weighted calculation on the newly added standby demands corresponding to all the typical scenes to obtain the new energy comprehensive standby capacity of the power system. According to the method, the distribution characteristics of various complex uncertain factors can be simulated, the calculation accuracy of the new energy reserve capacity of the power grid is enhanced, and the safety and economical efficiency of power grid operation are improved.

Description

Technical Field

[0001] The present invention belongs to the field of power system dispatching automation, and specifically relates to a method, system, device and storage medium for configuring new energy backup capacity of a power grid based on scenario simulation. Background Art

[0002] The large-scale grid connection of new energy has brought new challenges to the safe and economical operation of the power system. On the one hand, in order to cope with the uncertainty of new energy, the system needs to configure more backup; on the other hand, with the continuous increase in the penetration rate of new energy, traditional thermal power units are gradually replaced, and the traditional backup capacity is increasingly insufficient. Therefore, the reasonable configuration of backup is a key issue to ensure the safe and economical operation of the new power system.

[0003] The traditional power system reserve capacity configuration usually adopts a fixed percentage of the system load or the maximum capacity of a single machine. This method is simple and easy to implement, and can ensure that the system reaches a certain level of reliability. However, this type of method usually takes the safe and stable operation of the system as the primary consideration, and rarely considers the impact of new energy uncertainties on reserve capacity.

[0004] For the problem of spare capacity configuration after the access of new energy, the current method of probabilistic statistics of historical forecast error data of new energy is mostly used. This type of method is mainly based on the error distribution of historical forecast of new energy, lacks consideration of the possible output of new energy in the future, and cannot consider the spare capacity configuration requirements of power grid divisions under transmission congestion conditions.

[0005] Using multi-scenario technology to deal with random uncertainty problems can transform complex and difficult-to-describe uncertainty problems into multiple possible deterministic scenarios, thereby simplifying the difficulty of solving them. Currently, there have been studies on the application of multi-scenario technology to dispatch plan optimization, but power system dispatch requires deterministic results, and the uncertainty decision results of multiple scenarios are difficult to apply directly.

[0006] Therefore, how to take into account the characteristics of renewable energy power generation and grid operation in the future and calculate the renewable energy reserve capacity of the grid taking into account multiple scenarios has become a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0007] Purpose of the invention: The purpose of the present invention is to provide a method, system, device and storage medium for configuring the new energy reserve capacity of a power grid based on scenario simulation, generate scenarios based on the new energy forecast data of the operating day, optimize the scheduling and deduction of various new energy scenarios, and propose a method for configuring the new energy reserve capacity of the power grid under multiple scenarios, which will help improve the ability of the power grid to cope with the uncertainty of renewable energy power generation.

[0008] Technical solution: A method for configuring the backup capacity of new energy sources in a power grid based on scenario simulation of the present invention comprises:

[0009] Scenario set generation: The multi-scenario technology based on Markov chain is used to simulate the time correlation of the new energy prediction error over time, and generate a typical scenario set of new energy output;

[0010] Multi-scenario optimization deduction: Establish a grid optimization dispatching model under the scenario of renewable energy output, and use the grid optimization dispatching model to calculate the power generation capacity of each typical scenario in the typical scenario of renewable energy output and the power generation capacity of the base state scenario of the renewable energy prediction curve;

[0011] Multi-scenario backup demand calculation: Calculate the distance between the power generation capacity of each typical scenario and the power generation capacity of the base state scenario to obtain the additional backup demand corresponding to each typical scenario;

[0012] Calculation of comprehensive reserve capacity: Weighted calculation of the new reserve demand corresponding to all typical scenarios is performed to obtain the comprehensive reserve capacity of new energy in the power system.

[0013] Furthermore, a multi-scenario technology based on Markov chain is used to simulate the time correlation of the prediction error of renewable energy over time, and generate a typical scenario set of renewable energy output, including:

[0014] Obtain the forecast data of renewable energy power generation on the planned date, as well as the historical forecast data and actual output data of renewable energy power generation, and calculate the forecast error of renewable energy;

[0015] According to the prediction error of new energy, the multi-scenario technology based on Markov chain constructs the state vector of new energy prediction error, and Monte Carlo sampling is performed to obtain the output scenario of new energy power plants considering time correlation;

[0016] The scene reduction technology of the backward reduction method is used to reduce the output scenes of the renewable energy power plant;

[0017] Considering that the power grid contains multiple renewable energy power plants, a large number of single-plant output scenarios containing multiple renewable energy power plants are generated by freely arranging and combining the output scenarios of multiple renewable energy power plants that have undergone scenario reduction. Assuming that the total number of renewable energy power plants in a certain power grid is N w , the number of output scenarios of each new energy power plant is N p (p=1,2,…,N w ), the output scenarios of all new energy power plants are arranged and combined to obtain the final number of output scenarios of new energy power plants N a :

[0018]

[0019] The scenario reduction technology based on fuzzy C-means clustering is adopted. By comparing the Euclidean distance between each scenario and the central vector in a large number of single-plant output scenarios containing multiple renewable energy power plants, similar scenarios are merged to reduce the number of combined scenarios, thereby generating a typical scenario set containing multiple renewable energy power plants; J is defined as the Euclidean distance of fuzzy C-means clustering, and the expression is shown in the following formula:

[0020]

[0021] Where J is the Euclidean distance between each scene and the center vector; M is the number of scenes; C is the number of scenes after clustering; y w is the wth scene; v c is the cth (c=1,2,…,C) cluster scene center vector; μ cw For the wth scene y w To the cth cluster scene center vector v c The membership function of ; m is the convergence factor.

[0022] Furthermore, according to the prediction error of renewable energy, the multi-scenario technology based on Markov chain is used to construct the state vector of renewable energy prediction error, and Monte Carlo sampling is performed to obtain a large number of single-plant output scenarios of renewable energy power generation considering time correlation, including:

[0023] Let ε represent the new energy prediction error, f represent the probability density, discretize the new energy prediction error into n intervals, and record the state corresponding to the i-th interval as x i (i=1,2,…,n), the probability of occurrence of new energy prediction error in the i-th interval is S i ; The probability of occurrence S of new energy prediction error in each interval at time t i,t (i=1,2,…,n) constitutes the new energy prediction error state vector m at the current moment t , the expression is as follows:

[0024]

[0025] Where μ0 and σ0 represent the expectation and standard deviation of the normal distribution respectively; ε up,i and ε down,i They represent the upper and lower limits of the error in interval i respectively;

[0026] The change of random prediction error over time is regarded as a Markov process. It is known that the state X from the initial time t0 to the time t-1 t-1 , X t-2 , …, X t0 The corresponding state interval, and the state at the previous moment is X t-1 =x i , then the current state X t =xj The probability can be expressed as:

[0027] E(X t =x j |X t-1 ,X t-2 ,…,X t0 )=E(X t =x j |X t-1 =x i )=E ij

[0028] In the formula, E ij It means that the prediction error is from the state x at time t-1 i Transition to state x at time t j The one-step state transition probability can be obtained from statistical data, that is,

[0029]

[0030] In the formula, l ij It means that the historical data of uncertain quantity is analyzed by statistics, and the state x at time t-1 is i Transform to state x at time t j Number of occurrences;

[0031] After considering the time correlation, the prediction error state vector m at time t t Can be corrected to:

[0032]

[0033] Where E represents the one-step state transfer matrix, and E=(E ij ) n×n ,and

[0034] Furthermore, the objective function of the power grid optimization dispatching model under the renewable energy output scenario is as follows:

[0035]

[0036] In the formula, NT represents the number of time periods included in the optimal dispatch; NI represents the total number of thermal power units in the power system; C i represents the unit operating cost of thermal power unit i; P s,i,t represents the active power output of thermal power unit i in time period t under scenario s, which includes typical scenario and benchmark scenario; S i represents the startup cost of thermal power unit i; y s,i,t indicates whether the thermal power unit i has a sign of changing from shutdown to startup in time period t under scenario s; NW represents the total number of new energy units in the power system; C wrepresents the unit power abandonment cost of the new energy unit w; P s,w,t It represents the power abandonment of renewable energy unit w in time period t under scenario s.

[0037] Furthermore, the objective function of the power grid optimization dispatching model under the renewable energy output scenario is set with the following constraints:

[0038]

[0039] P i,min u s,i,t ≤P s,i,t ≤P i,max u s,i,t

[0040]

[0041] -Δ i ≤P s,i,t -P s,i,t-1 ≤Δ i

[0042]

[0043] Where, L t Represents the load demand of the power system in time period t; PI s,w,t represents the predicted power of the renewable energy unit w in time period t under scenario s; R 0,t represents the conventional reserve demand of the power system in period t; P i,max and P i,min Respectively represent the upper and lower limits of the output power of thermal power unit i; u s,i,t represents the start / stop status of thermal power unit i in time period t under scenario s; z s,i, τ indicates whether there is a sign of a change from startup to shutdown state of thermal power unit i in time period t under scenario s; UT i and DT i are the minimum start-up time and minimum shutdown time of thermal power unit i respectively; Δ i P represents the ramp rate of thermal power unit i in each period; l,max , P l,min They represent the upper and lower limits of the power flow of the lth transmission section respectively; N represents the set of grid computing nodes, P s,n,t represents the power generation power of grid computing node n at time period t under scenario s; l s,n,t represents the load power of grid computing node n at time period t under scenario s; S n,l,t It represents the sensitivity of the power of grid computing node n to the lth transmission section at time period t.

[0044] Furthermore, the distance between the power generation capacity of each typical scenario and the power generation capacity of the base state scenario is calculated. This distance is the newly added backup demand corresponding to each typical scenario. The calculation formula is:

[0045]

[0046] In the formula, R a,t represents the additional reserve demand of the power system in period t under typical scenario a; u a,i,t represents the start / stop status of thermal power unit i in period t under typical scenario a; u 0,i,t is the start / stop status of thermal power unit i in period t under the benchmark scenario; P i,max represents the upper limit of the output power of thermal power unit i; NI represents the total number of thermal power units in the power system.

[0047] Furthermore, the new reserve demand corresponding to all typical scenarios is weighted and calculated to obtain the comprehensive reserve capacity of new energy for the power system, including:

[0048]

[0049] In the formula, R t represents the comprehensive reserve capacity of new energy in the power system in period t; NS represents the total number of typical scenarios; R a,t represents the additional reserve demand of the power system in period t under typical scenario a; β a Represents the probability of typical scenario a.

[0050] Based on the same inventive concept, a grid new energy backup capacity configuration system based on scenario simulation of the present invention includes:

[0051] The scenario set generation module is used to simulate the time correlation of the new energy prediction error over time using the multi-scenario technology based on Markov chain, and generate a typical scenario set for new energy output;

[0052] The multi-scenario optimization deduction module is used to establish a power grid optimization dispatching model under the scenario of renewable energy output, and use the power grid optimization dispatching model to calculate the power generation capacity of each typical scenario in the typical scenario set of renewable energy output and the power generation capacity of the base state scenario of the renewable energy prediction curve;

[0053] The multi-scenario backup demand calculation module is used to calculate the distance between the power generation capacity of each typical scenario and the power generation capacity of the base state scenario, and obtain the newly added backup demand corresponding to each typical scenario;

[0054] The comprehensive reserve capacity calculation module is used to perform weighted calculation on the new reserve demands corresponding to all typical scenarios to obtain the comprehensive reserve capacity of new energy sources in the power system.

[0055] Based on the same inventive concept, a device for configuring the new energy backup capacity of a power grid based on scenario simulation of the present invention comprises a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the above-mentioned method for configuring the new energy backup capacity of a power grid based on scenario simulation.

[0056] Based on the same inventive concept, a computer-readable storage medium of the present invention stores a computer program thereon, and when the program is executed by a processor, the steps of the above-mentioned method for configuring the backup capacity of new energy sources in a power grid based on scenario simulation are implemented.

[0057] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are:

[0058] Considering the uncertainty factors of renewable energy power generation, multi-scenario optimization simulation is carried out, so that operators can understand the operation status and backup requirements under different scenarios, which helps to formulate emergency plans in advance;

[0059] Calculation of the grid's renewable energy reserve capacity based on the results of multi-scenario balance simulations can effectively simulate the distribution characteristics of prediction errors of various complex uncertain factors, achieve quantitative characterization of reserve demand, and enhance the grid's ability to cope with the uncertainty of renewable energy power generation.

[0060] The present invention helps to fully consider possible fluctuations in the output of renewable energy in the future, achieve accurate characterization of renewable energy regulation requirements under power and electricity balance and transmission channel constraints, improve the calculation accuracy of renewable energy reserve capacity, and improve the balancing capacity of the power grid and the level of renewable energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of a method for configuring power grid new energy reserve capacity based on scenario simulation disclosed in an embodiment of the present invention;

[0062] Figure 2 It is a structural schematic diagram of a grid new energy backup capacity configuration system based on scenario simulation disclosed in an embodiment of the present invention;

[0063] Figure 3 It is a structural schematic diagram of a power grid new energy backup capacity configuration device based on scenario simulation disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It will be understood by those skilled in the art that the purposes and advantages that can be achieved with the present invention are not limited to the specific description of the above beneficial effects, and the above and other purposes that can be achieved by the present invention will be more clearly understood based on the following detailed description.

[0065] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed in the present invention can be implemented in hardware, software or a combination of the two. Whether it is implemented in hardware or software depends on the specific application and design of the technical solution. Professional and technical personnel can use different methods to implement the functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0066] Reference to "embodiments" in the present invention means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0067] Example 1

[0068] See also Figure 1 , Figure 1 This is a flow chart of a method for configuring the backup capacity of new energy sources in a power grid based on scenario simulation disclosed in an embodiment of the present invention. Figure 1 The described method for configuring the reserve capacity of new energy sources in a power grid is applied to a power system, such as for calculating the reserve capacity of new energy sources in a power grid, and the embodiments of the present invention do not limit this. Figure 1 As shown, the scenario simulation-based method for configuring the grid new energy reserve capacity may include the following operations:

[0069] S1. Scenario set generation: The multi-scenario technology based on Markov chain is used to simulate the time correlation of the new energy prediction error over time, and generate a typical scenario set of new energy output.

[0070] The specific process of step S1 is as follows:

[0071] S1.1. Obtain the forecast data of renewable energy power generation on the planned date, as well as the historical forecast data and actual output data of renewable energy power generation, and calculate the forecast error of renewable energy.

[0072] S1.2. According to the prediction error of new energy, the multi-scenario technology based on Markov chain constructs the new energy prediction error state vector, and Monte Carlo sampling is performed to obtain the output scenario of the new energy power plant station considering the time correlation.

[0073] In this embodiment, the specific process of step S1.2 is as follows:

[0074] Let ε represent the new energy prediction error, f represent the probability density, discretize the new energy prediction error into n intervals, and record the state corresponding to the i-th interval as x i (i=1,2,…,n), the probability of occurrence of new energy prediction error in the i-th interval is S i ; The probability of occurrence S of new energy prediction error in each interval at time t i,t (i=1,2,…,n) constitutes the new energy prediction error state vector m at the current moment t , the expression is as follows:

[0075]

[0076] Where μ0 and σ0 represent the expectation and standard deviation of the normal distribution respectively; ε up,i and ε down,i Respectively represent the upper and lower limits of the error in interval i;

[0077] The change of random prediction error over time is regarded as a Markov process. It is known that the state X from the initial time t0 to the time t-1 t-1 , X t-2 , …, X t0 The corresponding state interval, and the state at the previous moment is X t-1 =x i , then the current state X t =x j The probability can be expressed as:

[0078] E(X t =x j |X t-1 ,X t-2 ,…,X t0 )=E(X t =x j |X t-1 =x i )=E ij

[0079] In the formula, E ij It means that the prediction error is from the state x at time t-1 i Transition to state x at time t j The one-step state transition probability can be obtained from statistical data, that is,

[0080]

[0081] In the formula, l ij It means that the historical data of uncertain quantity is analyzed by statistics, and the state x at time t-1 is i Transform to state x at time t j Number of occurrences;

[0082] After considering the time correlation, the prediction error state vector m at time t (t>t0) is t Can be corrected to:

[0083]

[0084] Where E represents the one-step state transfer matrix, and E=(E ij ) n×n ,and

[0085] S1.3. Use the scene reduction technology of the backward reduction method to reduce the output scenes of the new energy power plant.

[0086] S1.4. Considering that the power grid contains multiple renewable energy power plants, a large number of single-plant output scenarios containing multiple renewable energy power plants are generated by freely arranging and combining the output scenarios of multiple renewable energy power plants that have undergone scenario reduction. Assuming that the total number of renewable energy power plants in a certain power grid is N w , the number of output scenarios of each new energy power plant is N p (p=1,2,…,N w ), the output scenarios of all new energy power plants are arranged and combined to obtain the final number of output scenarios of new energy power plants N a :

[0087]

[0088] S1.5. Use the scenario reduction technology based on fuzzy C-means clustering to compare the Euclidean distance between each scenario and the center vector in a large number of single-plant output scenarios containing multiple renewable energy power plants, merge similar scenarios, reduce the number of combined scenarios, and thus generate a typical scenario set containing multiple renewable energy power plants. Define J as the Euclidean distance of fuzzy C-means clustering, expressed as follows:

[0089]

[0090] Where J is the Euclidean distance of fuzzy C-means clustering, which represents the Euclidean distance between each scene and the center vector; Where M is the number of scenes; C is the number of scenes after clustering; y w is the wth scene; v c is the cth (c=1,2,…,C) cluster scene center vector; μ cw For the wth scene y w To the cth cluster scene center vector v c The membership function of ; m is the convergence factor.

[0091] S2. Multi-scenario optimization simulation: Establish a grid optimization dispatching model under the scenario of renewable energy output, and use the grid optimization dispatching model to calculate the power generation capacity of each typical scenario in the typical scenario set of renewable energy output and the power generation capacity of the base state scenario of the renewable energy prediction curve.

[0092] In this embodiment, the objective function of the power grid optimization dispatching model in the renewable energy output scenario is as follows:

[0093]

[0094] In the formula, NT represents the number of time periods included in the optimal dispatch; NI represents the total number of thermal power units in the power system; C i represents the unit operating cost of thermal power unit i; P s,i,t represents the active power output of thermal power unit i in time period t under scenario s, which includes typical scenario and benchmark scenario; S i represents the startup cost of thermal power unit i; y s,i,t Indicates whether the thermal power unit i has a status change from shutdown to startup in time period t under scenario s; NW represents the total number of new energy units in the power system; C w represents the unit power abandonment cost of the new energy unit w; P s,w,t It represents the power abandonment of renewable energy unit w in time period t under scenario s.

[0095] The objective function of the grid optimization dispatching model under the renewable energy output scenario is set with the following constraints:

[0096]

[0097] P i,min u s,i,t ≤P s,i,t ≤P i,max u s,i,t

[0098]

[0099] -Δ i ≤P s,i,t -P s,i,t-1 ≤Δ i

[0100]

[0101] Where, L t Represents the load demand of the power system in time period t; PI s,w,t represents the predicted power of the renewable energy unit w in time period t under scenario s; R 0,t represents the conventional reserve demand of the power system in period t; P i,max and P i,minRespectively represent the upper and lower limits of the output power of thermal power unit i; u s,i,t represents the start / stop status of thermal power unit i in time period t under scenario s; z s,i, τ indicates whether there is a sign of a change from startup to shutdown state of thermal power unit i in time period t under scenario s; UT i and DT i are the minimum start-up time and minimum shutdown time of thermal power unit i respectively; Δ i P represents the ramp rate of thermal power unit i in each period; l,max , P l,min They represent the upper and lower limits of the power flow of the lth transmission section respectively; N represents the set of grid computing nodes, P s,n,t represents the power generation power of grid computing node n at time period t under scenario s; l s,n,t represents the load power of grid computing node n at time period t under scenario s; S n,l,t It represents the sensitivity of the power of grid computing node n to the lth transmission section at time period t.

[0102] S3. Calculation of backup demand in multiple scenarios: Calculate the distance between the power generation capacity of each typical scenario and the power generation capacity of the base scenario. This distance is the additional backup demand corresponding to each typical scenario.

[0103] In this embodiment, the calculation formula for the newly added standby demand corresponding to each typical scenario is as follows:

[0104]

[0105] In the formula, R a,t represents the additional reserve demand of the power system in period t under typical scenario a; u a,i,t represents the start / stop status of thermal power unit i in period t under typical scenario a; u 0,i,t is the start / stop status of thermal power unit i in period t under the benchmark scenario; P i,max represents the upper limit of the output power of thermal power unit i; NI represents the total number of thermal power units in the power system.

[0106] S4. Calculation of comprehensive reserve capacity: Perform weighted calculation on the new reserve demands corresponding to all typical scenarios to obtain the comprehensive reserve capacity of new energy in the power system.

[0107] In this embodiment, the calculation formula of the new energy comprehensive reserve capacity of the power system is as follows:

[0108]

[0109] In the formula, R t represents the comprehensive reserve capacity of new energy in the power system in period t; NS represents the total number of typical scenarios; R a,trepresents the additional reserve demand of the power system in period t under typical scenario a; β a Represents the probability of typical scenario a.

[0110] The present invention can simulate the distribution characteristics of various complex uncertain factors, enhance the calculation accuracy of the new energy reserve capacity of the power grid, and help improve the safety and economy of the power grid operation.

[0111] The following is a preferred implementation case to verify the scenario simulation-based power grid new energy reserve capacity configuration method proposed by the present invention. The preferred implementation case includes a calculation process of a provincial power grid new energy reserve capacity configuration using the method of the present invention. Its characteristics, purpose and advantages can be seen from the description of the embodiment.

[0112] Taking the calculation of the day-ahead renewable energy reserve capacity configuration of the provincial power grid as an example, the power control center needs to prepare the next day's dispatch plan based on the load forecast, renewable energy forecast and other boundary data for the next 96 hours, and determine the start and stop status and output plan of conventional units. In order to cope with the forecast error caused by the uncertainty of wind and solar renewable energy power generation, the dispatch plan preparation process needs to consider the reserve capacity constraints, and reserve power generation capacity through conventional units to meet the renewable energy reserve demand.

[0113] Firstly, according to the new energy forecast curve for 96 time periods on the planned day, as well as the historical data of new energy forecast output and actual new energy output in the past year, the probability distribution of new energy forecast error is calculated. The multi-scenario technology based on Markov chain is used to simulate the intermittent and volatility of new energy power generation over time, and 1,000 new energy output scenarios are generated. Clustering technology is used to reduce the scenarios, and 10 typical scenarios of new energy output are obtained.

[0114] Secondly, the optimization calculation of the day-ahead dispatch plan was performed for 10 typical scenarios of renewable energy output and the base-state scenario of the renewable energy forecast curve, taking into account the constraints such as system balance, power grid safety, and unit operation, and the corresponding thermal power generation capacity of each scenario was calculated. Since different renewable energy outputs are used as the boundaries of the day-ahead dispatch plan optimization calculation, the start-up and shutdown capacities of the thermal power units in each scenario are different, corresponding to different thermal power generation capacities.

[0115] Finally, the distance between the power generation capacity of each typical scenario and the power generation capacity of the base state scenario is calculated to obtain the new backup demand corresponding to each typical scenario. The new backup demand of multiple typical scenarios is weightedly calculated to obtain the comprehensive new energy backup capacity of the power grid.

[0116] The results of the grid's new energy reserve capacity configuration obtained based on scenario deduction are analyzed. The generated scenario set describes the uncertainty of new energy. The operators can have a more comprehensive understanding of the new energy output that may occur the next day, and then configure the corresponding reserve capacity in advance. Through the deduction of multiple new energy output scenarios, an anti-interference reserve capacity configuration scheme is optimized. Based on this reserve capacity, the adjustment space formed by the upper and lower output ranges of the units can cope with the fluctuations in new energy generation to the greatest extent, providing a guarantee for the safe and economic dispatch of the grid after large-scale new energy access.

[0117] This method realizes the calculation of the configuration of new energy reserve capacity of the power grid based on scenario deduction. It does not require the participation of a large amount of manpower, and the calculation speed can meet the needs of practical applications. It effectively solves the problems that the traditional calculation of new energy reserve demand is difficult to adapt to various complex error distribution characteristics, relies on experience, and has low efficiency. It has broad promotion prospects.

[0118] Example 2

[0119] See also Figure 2 , Figure 2 The present invention discloses a schematic diagram of a system for configuring a power grid new energy reserve capacity based on scenario simulation, which can calculate the power grid new energy reserve capacity, and specifically includes:

[0120] The scenario set generation module is used to simulate the time correlation of the new energy prediction error over time using the multi-scenario technology based on Markov chain, and generate a typical scenario set for new energy output;

[0121] The multi-scenario optimization deduction module is used to establish a power grid optimization dispatching model under the scenario of renewable energy output, and use the power grid optimization dispatching model to calculate the power generation capacity of each typical scenario in the typical scenario set of renewable energy output and the power generation capacity of the base state scenario of the renewable energy prediction curve;

[0122] The multi-scenario backup demand calculation module is used to calculate the distance between the power generation capacity of each typical scenario and the power generation capacity of the base state scenario, and obtain the newly added backup demand corresponding to each typical scenario;

[0123] The comprehensive reserve capacity calculation module is used to perform weighted calculation on the new reserve demands corresponding to all typical scenarios to obtain the comprehensive reserve capacity of new energy sources in the power system.

[0124] In an optional embodiment, a method for configuring the new energy backup capacity of a power grid based on scenario simulation: a) using Markov chain technology to model the time correlation of new energy prediction errors, and generating a set of typical scenarios for new energy output; b) establishing a power grid optimization scheduling model under new energy output scenarios, and deducing and calculating the power generation capacity corresponding to each scenario; c) calculating the distance between the power generation capacity of each typical scenario and the power generation capacity of the base state scenario, and obtaining the newly added backup demand corresponding to each typical scenario; d) performing weighted calculation on the newly added backup demands of multiple typical scenarios, and obtaining the comprehensive new energy backup capacity of the power system.

[0125] Example 3

[0126] See also Figure 3 , Figure 3 This is a schematic diagram of a structure of a power grid new energy backup capacity configuration device based on scenario simulation disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to the power system, such as for calculating the reserve capacity of new energy sources in the power grid, etc., and the embodiments of the present invention are not limited thereto.

[0127] like Figure 3 As shown, the device may include a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the method described in the above embodiment and can achieve the technical effect consistent with the above method.

[0128] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). A program / utility having a set (at least one) of program modules may be stored in, for example, the memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include the implementation of a network environment. The program modules typically perform the functions and / or methods in the embodiments described herein.

[0129] The processor executes various functional applications and data processing by running the program stored in the memory, such as implementing the method provided in the first embodiment of the present invention.

[0130] Example 4

[0131] Embodiment 4 of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in the above embodiment are implemented and the technical effect consistent with the above method can be achieved.

[0132] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0133] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0134] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0135] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or combinations thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0136] Of course, the storage medium containing computer executable instructions provided by an embodiment of the present invention, whose computer executable instructions are not limited to the above method operations, can also execute related operations in the method provided by any embodiment of the present invention.

[0137] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for configuring the backup capacity of new energy sources in a power grid based on scenario simulation, characterized in that: include: Scenario set generation: The Markov chain-based multi-scenario technology is used to simulate the time correlation of the new energy prediction error over time, and generate a typical scenario set for new energy output; Multi-scenario optimization deduction: Establish a grid optimization dispatching model under the scenario of renewable energy output, and use the grid optimization dispatching model to calculate the power generation capacity of each typical scenario in the typical scenario of renewable energy output and the power generation capacity of the base state scenario of the renewable energy prediction curve; Multi-scenario backup demand calculation: Calculate the distance between the power generation capacity of each typical scenario and the power generation capacity of the base scenario. This distance is the additional backup demand corresponding to each typical scenario. Calculation of comprehensive reserve capacity: Weighted calculation of the new reserve demand corresponding to all typical scenarios is performed to obtain the comprehensive reserve capacity of new energy in the power system.

2. The method for configuring the backup capacity of new energy sources in a power grid based on scenario simulation according to claim 1, characterized in that: The Markov chain-based multi-scenario technology is used to simulate the time correlation of the new energy prediction error over time and generate a typical scenario set of new energy output, including: Obtain the forecast data of renewable energy power generation on the planned date, as well as the historical forecast data and actual output data of renewable energy power generation, and calculate the forecast error of renewable energy; According to the prediction error of new energy, the multi-scenario technology based on Markov chain constructs the state vector of new energy prediction error, and Monte Carlo sampling is performed to obtain the output scenario of new energy power plants considering time correlation; The scene reduction technology of the backward reduction method is used to reduce the output scenes of the renewable energy power plant; Considering that the power grid contains multiple renewable energy power plants, a large number of single-plant output scenarios containing multiple renewable energy power plants are generated by freely arranging and combining the output scenarios of multiple renewable energy power plants that have undergone scenario reduction. Assuming that the total number of renewable energy power plants in a certain power grid is N w , the number of output scenarios of each new energy power plant is N p (p=1,2,…,N w ), the output scenarios of all new energy power plants are arranged and combined to obtain the final number of output scenarios of new energy power plants N a : The scenario reduction technology based on fuzzy C-means clustering is adopted. By comparing the Euclidean distance between each scenario and the central vector in a large number of single-plant output scenarios containing multiple renewable energy power plants, similar scenarios are merged to reduce the number of combined scenarios, thereby generating a typical scenario set containing multiple renewable energy power plants; J is defined as the Euclidean distance of fuzzy C-means clustering, and the expression is shown in the following formula: Where M is the number of scenes; C is the number of scenes after clustering; y w is the wth scene; v c is the cth (c=1,2,…,C) cluster scene center vector; μ cw For the wth scene y w To the cth cluster scene center vector v c The membership function of ; m is the convergence factor.

3. The method for configuring the backup capacity of new energy sources in a power grid based on scenario simulation according to claim 2 is characterized in that: According to the new energy prediction error, the new energy prediction error state vector is constructed based on the multi-scenario technology of Markov chain, and Monte Carlo sampling is performed to obtain the output scenario of the new energy power plant station considering the time correlation, including: Let ε represent the new energy prediction error, f represent the probability density, discretize the new energy prediction error into n intervals, and record the state corresponding to the i-th interval as x i (i=1,2,…,n), the probability of occurrence of new energy prediction error in the i-th interval is S i ; The probability of occurrence S of new energy prediction error in each interval at time t i,t (i=1,2,…,n) constitutes the new energy prediction error state vector m at the current moment t , the expression is as follows: Where μ0 and σ0 represent the expectation and standard deviation of the normal distribution respectively; ε up,i and ε down,i Respectively represent the upper and lower limits of the error in interval i; The change of random prediction error over time is regarded as a Markov process. It is known that the state X from the initial time t0 to the time t-1 t-1 , X t-2 , …, X t0 The corresponding state interval, and the state at the previous moment is X t-1 =x i , then the current state X t =x j The probability can be expressed as: E(X t =x j |X t-1 ,X t-2 ,…,X t0 )=E(X t ex j |X t-1 =x i )=E ij In the formula, E ij It means that the prediction error is from the state x at time t-1 i Transition to state x at time t j The one-step state transition probability can be obtained from statistical data, that is, In the formula, l ij It means that the historical data of uncertain quantity is analyzed by statistics, and the state x at time t-1 is i Transform to state x at time t j Number of occurrences; After considering the time correlation, the prediction error state vector m at time t t Can be corrected to: Where E represents the one-step state transfer matrix, and E=(E ij ) n×n ,and 4. The method for configuring the backup capacity of new energy sources in a power grid based on scenario simulation according to claim 1, characterized in that: The objective function of the grid optimization dispatching model under the renewable energy output scenario is as follows: In the formula, NT represents the number of time periods included in the optimal dispatch; NI represents the total number of thermal power units in the power system; C i represents the unit operating cost of thermal power unit i; P s,i,t represents the active power output of thermal power unit i in time period t under scenario s, which includes typical scenario and benchmark scenario; S i represents the startup cost of thermal power unit i; y s,i,t indicates whether the thermal power unit i has a sign of changing from shutdown to startup in time period t under scenario s; NW represents the total number of new energy units in the power system; C w represents the unit power abandonment cost of the new energy unit w; P s,w,t It represents the power abandonment of renewable energy unit w in time period t under scenario s.

5. The method for configuring the backup capacity of new energy sources in power grid based on scenario simulation according to claim 4 is characterized in that: The objective function of the grid optimization dispatching model under the renewable energy output scenario is set with the following constraints: P i,min in s,i,t ≤P s,i,t ≤P i,max in s,i,t -D i ≤P s,i,t -P s,i,t-1 ≤Δ i Where, L t Represents the load demand of the power system in time period t; PI s,w,t represents the predicted power of the renewable energy unit w in time period t under scenario s; R 0,t represents the conventional reserve demand of the power system in period t; P i,max and P i,min Respectively represent the upper and lower limits of the output power of thermal power unit i; u s,i,t represents the start / stop status of thermal power unit i in time period t under scenario s; z s,i, τ indicates whether there is a sign of a change from startup to shutdown state of thermal power unit i in time period t under scenario s; UT i and DT i are the minimum start-up time and minimum shutdown time of thermal power unit i respectively; Δ i P represents the ramp rate of thermal power unit i in each period; l,max , P l,min They represent the upper and lower limits of the power flow of the lth transmission section respectively; N represents the set of grid computing nodes, P s,n,t represents the power generation power of grid computing node n at time period t under scenario s; l s,n,t represents the load power of grid computing node n at time period t under scenario s; S n,l,t It represents the sensitivity of the power of grid computing node n to the lth transmission section at time period t.

6. The method for configuring the backup capacity of new energy sources in a power grid based on scenario simulation according to claim 1, characterized in that: Calculate the distance between the power generation capacity of each typical scenario and the power generation capacity of the base state scenario. This distance is the new backup demand corresponding to each typical scenario. The calculation formula is: In the formula, R a,t represents the additional reserve demand of the power system in period t under typical scenario a; u a,i,t represents the start / stop status of thermal power unit i in period t under typical scenario a; u 0,i,t is the start / stop status of thermal power unit i in period t under the benchmark scenario; P i,max Indicates the upper limit of the output power of thermal power unit i; NI represents the total number of thermal power units in the power system.

7. The method for configuring the backup capacity of new energy sources in a power grid based on scenario simulation according to claim 1, characterized in that: The new reserve demand corresponding to all typical scenarios is weighted and calculated to obtain the comprehensive reserve capacity of new energy sources in the power system, including: In the formula, R t represents the comprehensive reserve capacity of renewable energy in the power system in period t; NS represents the total number of typical scenarios; R a,t represents the additional reserve demand of the power system in period t under typical scenario a; β a Represents the probability of typical scenario a.

8. A grid new energy backup capacity configuration system based on scenario simulation, characterized in that: include: The scenario set generation module is used to simulate the time correlation of the new energy prediction error over time using the multi-scenario technology based on Markov chain, and generate a typical scenario set for new energy output; The multi-scenario optimization deduction module is used to establish a power grid optimization dispatching model under the scenario of renewable energy output, and use the power grid optimization dispatching model to calculate the power generation capacity of each typical scenario in the typical scenario set of renewable energy output and the power generation capacity of the base state scenario of the renewable energy prediction curve; The multi-scenario backup demand calculation module is used to calculate the distance between the power generation capacity of each typical scenario and the power generation capacity of the base state scenario, and obtain the newly added backup demand corresponding to each typical scenario; The comprehensive reserve capacity calculation module is used to perform weighted calculation on the new reserve demands corresponding to all typical scenarios to obtain the comprehensive reserve capacity of new energy sources in the power system.

9. A device for configuring the backup capacity of new energy sources in a power grid based on scenario simulation, characterized in that: The electronic device comprises a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the method for configuring the backup capacity of new energy sources in a power grid based on scenario simulation as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for configuring the backup capacity of new energy sources in a power grid based on scenario simulation as claimed in any one of claims 1 to 7.

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