New energy openable capacity rapid evaluation method, system and device considering power grid security constraint, and medium

Abstract

The invention discloses a new energy openable capacity rapid assessment method, system and device considering power grid security constraints and a medium, and belongs to the technical field of new energy openable capacity assessment, and the method comprises the steps: building a power grid model; power grid openable capacity calculation related constraint conditions of power grid safety are quantitatively considered; selecting zero openable capacity as an initial value, simulating section operation data of the new energy unit by adopting a Markov chain Monte Carlo method, and judging whether the openable capacity meets a constraint condition or not; if the constraint conditions are met, the open capacity is increased according to the increasing step length by adopting a heuristic method, and if the constraint conditions are not met, the open capacity of the new energy is obtained by adopting a dichotomy method. According to the method, the safety constraint of the power grid is comprehensively considered, the Monte Carlo sampling and the dichotomy are combined, the openable capacity of the power grid after new energy access can be rapidly and accurately evaluated, a scientific basis is provided for power grid planning, operation and new energy scheduling, and therefore the operation efficiency of the power grid and the utilization rate of the new energy are improved.

Description

Technical Field

[0001] This invention relates to the field of renewable energy open capacity assessment technology, specifically to a method, system, device, and medium for rapid assessment of renewable energy open capacity taking into account grid security constraints. Background Technology

[0002] As the global transition to clean energy accelerates, the penetration rate of new energy sources such as wind power and solar power in the power system is rapidly increasing. Due to their advantages such as being clean, environmentally friendly, and renewable, new energy power generation is gradually becoming an important component of the power system. However, the intermittent and uncertain nature of new energy output poses numerous challenges to the safe and stable operation of the power grid.

[0003] Traditional grid capacity assessment methods primarily rely on deterministic load forecasting and the dispatchability characteristics of conventional generating unit models. However, with the widespread integration of new energy sources, the grid's operating environment has become more complex. New energy output is significantly affected by weather conditions and seasonal variations, leading to frequent fluctuations in power generation and increasing the pressure on the grid in terms of power balance, frequency regulation, and voltage stability. Therefore, assessing the grid's available capacity requires comprehensive consideration of various complex constraints, including node voltage, power flow distribution, frequency stability, and the N-1 failover criterion, to ensure the safe and stable operation of the grid.

[0004] Existing methods for assessing the open capacity of renewable energy sources are mostly based on traditional power flow analysis and Monte Carlo simulation. These methods typically suffer from high computational complexity and low assessment efficiency when dealing with the stochastic nature of renewable energy output and complex multi-constraint conditions, making them unsuitable for the real-time assessment needs of large-scale power grids. Furthermore, traditional methods often lack a comprehensive consideration of grid security, especially in responding to sudden faults, failing to effectively assess the impact of the N-1 safety criterion on system stability, thus limiting their application in grids with a high proportion of renewable energy integration.

[0005] Therefore, there is an urgent need for a method that can comprehensively consider grid security constraints and is efficient and accurate in rapidly assessing the grid's available capacity after the integration of new energy sources. This invention innovatively introduces a hybrid algorithm combining Markov chain Monte Carlo method with trial and error and binary search methods, aiming to address the shortcomings of existing technologies in handling uncertainties in new energy output, multiple security constraints, and rapid computational capabilities. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem addressed by this invention is how to solve the issue that existing methods for assessing the open capacity of new energy sources are mostly based on traditional power flow analysis and Monte Carlo simulation. These methods typically suffer from high computational complexity and low assessment efficiency when dealing with the randomness of new energy output and complex multi-constraint conditions, making it difficult to meet the real-time assessment requirements of large-scale power grids. Furthermore, traditional methods often lack a comprehensive consideration of grid security, especially in responding to sudden faults, failing to effectively assess the impact of the N-1 safety criterion on system stability, thus limiting their application in high-proportion new energy grid integration.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: a rapid assessment method for the open capacity of new energy sources considering grid security constraints, comprising: establishing a grid model, including a new energy unit model, a conventional unit model, a load base model, a line model, and a transformer model; quantifying the relevant constraints for calculating the open capacity of the grid considering grid security; selecting zero open capacity as the initial value, simulating the cross-sectional operating data of the new energy unit model using the Markov chain Monte Carlo method, and performing power flow calculations in conjunction with the established grid model to determine whether the calculated grid power flow, voltage, current, and frequency under the open capacity meet the constraints, and further determining whether they meet the N-1 fault safety analysis; if the constraints are met, using a trial-and-error method to increase the open capacity in incremental steps, and repeatedly verifying the constraints and performing N-1 fault safety judgments for each trial capacity value; if not met, using a bisection method to backtrack and calculate until all constraints are met, thus obtaining the open capacity of new energy sources.

[0009] As a preferred embodiment of the rapid assessment method for the open capacity of new energy sources that takes into account grid security constraints as described in this invention, the establishment of the grid model includes constructing a multi-type parameter model that characterizes the output characteristics, conventional regulation capabilities, load dispatching characteristics, and electrical topology of new energy sources, supporting power flow calculation and constraint verification.

[0010] As a preferred embodiment of the rapid assessment method for the openable capacity of new energy sources that takes into account grid security constraints as described in this invention, the quantitative grid security constraints include boundary limits describing voltage, current, frequency, and transmission equipment capacity, which serve as constraint benchmarks for judging the feasibility of openable capacity.

[0011] As a preferred embodiment of the rapid assessment method for the open capacity of new energy sources that takes into account grid security constraints as described in this invention, the step of simulating the cross-sectional operation data of the new energy unit model using the Markov chain Monte Carlo method includes constructing a state transition matrix based on the historical operation data of new energy sources and generating a typical power sequence that fits the uncertainty of actual output using random sampling.

[0012] As a preferred embodiment of the rapid assessment method for the open capacity of new energy sources considering grid security constraints described in this invention, the method of simulating the cross-sectional operating data of the new energy unit model using the Markov chain Monte Carlo method includes: simulating the cross-sectional operating data of the new energy unit model using the Markov chain Monte Carlo method, performing power flow calculations in conjunction with the established grid model, and obtaining the grid power flow, voltage, current, and frequency parameters under the current open capacity; comparing the calculated node voltage and branch voltage difference, transmission capacity of transmission lines and transformers, branch current, and frequency fluctuation values ​​with the constraint conditions to determine whether the calculated grid power flow, voltage, current, and frequency under the open capacity meet the constraint conditions; if all constraints are met, then proceeding to N-1 fault safety analysis, including disconnecting any branch or generator and recalculating the power flow; if no islanding structure is formed, then assessing whether the transmission power of the branch under fault conditions exceeds the set safety limit to determine whether there is a risk of exceeding the limit; the constraint conditions include the upper and lower limits of node voltage and the voltage constraints at the beginning and end of the branch, the maximum transmission capacity constraints of transmission lines and transformers, the branch current constraints, and the grid frequency constraints.

[0013] This preferred scheme combines the Markov chain Monte Carlo method with the power grid model to conduct new energy output simulation and power flow calculation during the open capacity calculation process. While fully considering the uncertainty of new energy output, it can systematically judge whether the constraints such as voltage, current, and frequency have been broken. Furthermore, it adds an N-1 fault analysis step to ensure that the obtained capacity still has grid security under extreme operating conditions, significantly improving the comprehensiveness and reliability of capacity assessment.

[0014] As a preferred embodiment of the rapid assessment method for the openable capacity of new energy sources that takes into account grid security constraints as described in this invention, the step of increasing the openable capacity using a trial method with incremental steps includes: gradually increasing the openable capacity using a trial method with a preset step size, and performing new energy operation data simulation, power flow calculation, constraint condition verification, and N-1 fault safety judgment on each trial capacity value, until a certain openable capacity value fails to meet the constraint conditions for the first time, which is then determined as the trial over-limit capacity.

[0015] This preferred solution uses a trial-and-error method to gradually increase the available capacity of new energy sources with incremental step sizes, supplemented by full-process constraint verification and N-1 security analysis corresponding to each step capacity. This can quickly approach the system capacity boundary without increasing computational complexity, significantly improving capacity calculation efficiency. At the same time, it avoids computational waste or evaluation distortion caused by loading a large capacity at once, enhancing the real-time performance and stability of capacity assessment.

[0016] As a preferred embodiment of the rapid assessment method for renewable energy open capacity considering grid security constraints described in this invention, the step of using a bisection method to backtrack and calculate until all constraints are met includes: when the open capacity is increased to a certain value using a trial method and the constraints are not met, the open capacity that does not meet the constraints is determined as the trial over-limit capacity, and the open capacity value that met the constraints in the last time is recorded as the trial boundary capacity; within the interval formed by the trial over-limit capacity and the trial boundary capacity, an intermediate capacity is selected as a new candidate capacity based on the bisection method, and the operation data of the renewable energy unit model section is simulated using the Markov chain Monte Carlo method, combined with the established grid model to perform power flow calculation, to determine whether the power flow, voltage, current and frequency of the grid under the candidate capacity meet the constraints, and further N-1 fault safety analysis is performed; if all constraints are met, the intermediate capacity is used as the new boundary capacity to continue narrowing the interval; if not, the intermediate capacity is used as the new over-limit capacity to continue iterating until the open capacity interval meets the set accuracy, and the renewable energy open capacity that finally meets all constraints is output.

[0017] This preferred scheme introduces a bisection backoff calculation mechanism after obtaining the capacity boundary using the trial method. This mechanism can quickly locate the final acceptable new energy capacity value between the known over-limit capacity and the boundary capacity, making the capacity value convergence process more stable and the precision control more flexible. It avoids misjudgment or evaluation failure caused by excessive capacity step, ensures that the final calculation result meets all grid security constraints, and improves the credibility and engineering practicality of the evaluation result.

[0018] This invention provides a rapid assessment system for the open capacity of new energy sources that takes into account grid security constraints.

[0019] To address the aforementioned technical problems, this invention provides the following technical solution: a rapid assessment system for the open capacity of new energy sources considering grid security constraints, comprising: a grid modeling module, a constraint condition module, a simulation judgment module, and a capacity iteration module; the grid modeling module is used to establish a grid model, including a new energy unit model, a conventional unit model, a load base model, a line model, and a transformer model; the constraint condition module is used to quantify the relevant constraints for calculating the open capacity of the grid considering grid security; the simulation judgment module is used to select the open capacity as zero as the initial value, use the Markov chain Monte Carlo method to simulate the cross-sectional operating data of the new energy unit model, combine it with the established grid model to perform power flow calculation, and determine whether the calculated grid power flow, voltage, current, and frequency under the open capacity meet the constraint conditions, and further determine whether they meet the N-1 fault safety analysis; the capacity iteration module is used to increase the open capacity by incremental steps using a trial method if the constraint conditions are met, and to repeatedly verify the constraint conditions and perform N-1 fault safety judgment for each trial capacity value; if not met, a binary search method is used to backtrack and calculate until all constraint conditions are met, thus obtaining the open capacity of new energy sources.

[0020] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the method for rapid assessment of the open capacity of new energy sources taking into account grid security constraints.

[0021] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for rapid assessment of the open capacity of new energy sources taking into account grid security constraints.

[0022] The beneficial effects of this invention are as follows: This invention comprehensively considers power grid security constraints and combines Monte Carlo sampling and the dichotomy method to quickly and accurately assess the open capacity of the power grid after the access of new energy sources. This provides a scientific basis for power grid planning, operation and new energy dispatch, thereby improving the operating efficiency of the power grid and the utilization rate of new energy sources. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 The above is a flowchart of a method for rapid assessment of the open capacity of new energy sources that takes into account grid security constraints, provided as an embodiment of the present invention.

[0025] Figure 2 A circuit model diagram of a rapid assessment method for the open capacity of new energy sources that takes into account grid security constraints, provided as an embodiment of the present invention.

[0026] Figure 3 A transformer equivalent model diagram is provided as an embodiment of the present invention for a rapid assessment method of the open capacity of new energy sources taking into account grid security constraints.

[0027] Figure 4 A flowchart of the renewable energy output curve based on MCMC is provided as an embodiment of the present invention for a rapid assessment method of renewable energy open capacity taking into account grid security constraints.

[0028] Figure 5 The flowchart illustrates the steps of an N-1 security analysis method for a rapid assessment of the openable capacity of new energy sources that takes into account grid security constraints, as provided in an embodiment of the present invention.

[0029] Figure 6 The flowchart illustrates a method for rapidly calculating the openable capacity of new energy sources using a bisection approach, which takes into account grid security constraints, as an embodiment of the present invention.

[0030] Figure 7 This is a schematic diagram of a scheme for a rapid assessment system for the open capacity of new energy sources that takes into account grid security constraints, provided as an embodiment of the present invention. Detailed Implementation

[0031] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0032] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for rapid assessment of the open capacity of new energy sources taking into account grid security constraints, including:

[0033] S1. Establish a power grid model, including a new energy unit model, a conventional unit model, a load foundation model, a line model, and a transformer model.

[0034] S2. Quantitatively consider the constraints related to the calculation of the open capacity of the power grid, taking into account power grid security.

[0035] S3. Select zero open capacity as the initial value, use the Markov chain Monte Carlo method to simulate the cross-sectional operation data of the new energy unit model, combine it with the established power grid model to perform power flow calculation, determine whether the calculated power flow, voltage, current and frequency of the power grid under the open capacity meet the constraints, and further determine whether the N-1 fault safety analysis is satisfied.

[0036] S4. If the constraints are met, the available capacity is increased by an incremental step using a trial method. The constraints are verified and N-1 fault safety judgment is repeated for each trial capacity value. If the constraints are not met, the binary search method is used to backtrack and calculate until all constraints are met, thus obtaining the available capacity of new energy.

[0037] It should be noted that with the large-scale integration of renewable energy into the grid, system frequency stability, voltage compliance, and line load capacity face continuous challenges. Traditional capacity assessment methods are lagging or crude in dealing with the uncertainty of renewable energy output, N-1 operating conditions, and frequency-voltage coupling constraints, resulting in inaccurate capacity assessment results and insufficient safety margins.

[0038] Therefore, to address the aforementioned problems, this invention constructs a full-structure power grid model, introduces multiple types of security constraints, employs Markov Chain Monte Carlo (MCMC) output modeling and power flow simulation techniques, and combines a joint iterative strategy of trial and error and bisection methods. This enables it to accurately simulate the output behavior of new energy sources under actual operating conditions, gradually approaching the power grid safety boundary, and ultimately obtaining the openable capacity of new energy sources that meets the requirements of power grid operation safety. It has the advantages of high accuracy, strong adaptability, and high evaluation efficiency.

[0039] Example 2, refer to Figures 2-6 As an embodiment of the present invention, based on the previous embodiment, a method for rapid assessment of the open capacity of new energy sources taking into account grid security constraints is provided, including:

[0040] In the embodiments of this application, step S1 establishes a power grid model, including a new energy unit model, a conventional unit model, a load foundation model, a line model, and a transformer model.

[0041] Construct multi-type parameter models to characterize the output characteristics of new energy sources, conventional regulation capabilities, load dispatch characteristics, and electrical topology, supporting power flow calculation and constraint verification.

[0042] Specifically, the model for new energy generating units is as follows:

[0043] The output of a solar photovoltaic (PV) generator is mainly affected by sunlight intensity, cell junction temperature, and ambient temperature. The mathematical model for PV output can be expressed as:

[0044]

[0045] Among them, P PV (t) represents the output power of the photovoltaic unit at time t, k T P is the temperature coefficient. SET S represents the rated output power of the photovoltaic unit. SET Let S(t) be the standard irradiance of the photovoltaic (PV) unit, S(t) be the irradiance of the PV unit at time t, and T(t) be the ambient temperature at time t. SET This refers to the standard ambient temperature.

[0046] Photovoltaic power output is subject to uncertainty due to variations in solar irradiance. It is generally assumed that solar irradiance over a given time scale follows a Beta distribution, with the function:

[0047]

[0048] Where a is the shape parameter of light intensity, b is the distribution parameter of light intensity, μ is the average value of solar irradiance data, σ is the standard deviation of solar irradiance data, Γ is the gamma function, and γ is the light intensity of the photovoltaic device at a certain moment. max This represents the maximum light intensity.

[0049] Wind power generation converts wind energy into electrical energy through wind turbines. The basic principle is that wind energy drives the blades on the turbine to rotate, converting wind energy into mechanical energy. The rotating blades then drive the generator's main shaft to rotate, which in turn converts it into electrical energy. Based on the power generation principle, the mechanical output power of a wind turbine is mainly affected by parameters such as wind speed, blade radius, and air density. Its mathematical expression is:

[0050]

[0051] Among them, P M This refers to the mechanical output power of the fan. R represents the scanned area of ​​the blade. WT Where ρ is the blade radius, ν is the air density, and C is the wind speed. p This represents the wind energy utilization coefficient.

[0052] The active power of a wind turbine is related to natural factors such as wind speed and direction, as well as control strategies, with real-time wind speed being the most significant factor. To ensure the safe operation of the turbine, the wind speed experienced by the blades cannot increase indefinitely; it is necessary to switch out of operation when necessary. The output power of the wind turbine varies with wind speed, as shown by the following functional relationship:

[0053]

[0054] Among them, P rated v is the rated power of the fan, v is the real-time wind speed, v inTo cut off the wind speed, v rated For the rated wind speed, v out To cut off the wind speed. When ν < ν in At that time, due to the low wind speed, the fan has no power output. in ≤ν≤ν rated At that time, the output power of the fan increases with the increase of wind speed, when ν rated ≤ν<ν out At this time, the wind turbine will continue to output rated power. If the wind speed continues to increase and exceeds the cut-out wind speed, the wind turbine will be taken out of operation and disconnected from the power grid to avoid an accident.

[0055] Because the output of wind turbines is affected by changes in wind speed, it also exhibits uncertainty. It is generally believed that the wind speed for wind turbines roughly follows a two-parameter Weibull distribution, the function of which is:

[0056]

[0057] k=(σ / μ) -1.086

[0058] Where c is the actual wind speed, Γ is the gamma function, k is the shape parameter of the curve distribution, μ is the average wind speed, and σ is the root mean square deviation of the wind speed.

[0059] Furthermore, the conventional unit model is established as follows:

[0060]

[0061] Where, N G P represents the total number of nodes in the system's conventional unit model. G,i and Q G,i These represent the active and reactive power outputs of node i in the conventional unit model, respectively; P G0,i k represents the rated active power output of node i in the conventional unit model; G,i P is the unit regulation coefficient of node i in the conventional unit model; Δf is the system frequency deviation; i,max and P i,min These represent the maximum and minimum active power outputs of node i in the conventional unit model, respectively; Q i,max and Q i,min These represent the maximum and minimum reactive power output of node i in the conventional unit model, respectively.

[0062] Furthermore, the load basis model is as follows:

[0063] During the scheduling period, let the interval where the transferable load is located be . The duration of translation is t. * The translation time interval can be represented by the following set:

[0064]

[0065] The power after the load that can be shifted participates in the dispatch is:

[0066]

[0067] Where t is the total scheduling duration, For the state variables scheduled to time i, when When, it indicates that the power shifts at that moment; when When the value is zero, it indicates that no translation occurs; This refers to the power before loads that can be moved participate in scheduling.

[0068] Transferable load can be partially or entirely shifted to other time periods within the scheduling cycle, but the total load remains constant. Let the time interval containing the transferable load be denoted as . The power of transferable loads is subject to the following constraints:

[0069]

[0070] in, For the state variables scheduled to time j, when When, it indicates that power transfer occurs at that moment; when When P is in that state, no power transfer occurs; trs,min P trs,max These are the minimum and maximum output power of the transferable load, respectively.

[0071] For loads that can be reduced, the load power before and after reduction is:

[0072]

[0073] in, In order to reduce the load power before it is reduced, In order to reduce the load and participate in the reduced load power, This refers to the state variables scheduled up to time z. When, it indicates that power reduction occurs at that moment; when When γ is present, it indicates that no reduction occurs; z The reduction factor that can reduce the load and γ z ∈[0,1].

[0074] Furthermore, establish circuit models and transformer models, where the circuit model is as follows: Figure 2 As shown, i and j are the two endpoints of the line connection, Z ij aa Z ij bb Z ij ccZ represents the self-impedance of phases a, b, and c of the line between endpoints i and j, respectively. ij ab Z ij bc Z ij ac These are the mutual impedances between the i and j endpoints, respectively.

[0075] Use Z l To represent the series impedance of the line, then

[0076]

[0077] Equivalent admittance matrix Y of the line L for

[0078]

[0079] Transformer model such as Figure 3 As shown, Y m Z is the admittance of the excitation branch. T The copper loss in the winding branch is S, and the transformer capacity is S. N The rated voltage on the high-voltage side is U N The rated current on the high-voltage side is I. N The short-circuit voltage percentage is U k The no-load current is I0%, and the short-circuit loss is P. k The no-load loss is P0.

[0080] The winding copper loss is approximately equal to the short-circuit loss, therefore the calculation formula is as follows:

[0081]

[0082] Among them, R T X is the equivalent resistance of the transformer. T This is the equivalent reactance of the transformer.

[0083] The no-load loss of a transformer is approximately equal to the iron loss, therefore the calculation formula is as follows:

[0084]

[0085] Among them, G T For magnetizing conductance, B T It is the magnetizing inductor.

[0086] In an alternative implementation, establishing a power grid model may further include constructing a regulation capacity model for conventional generating units, setting rated output, maximum / minimum active and reactive power boundaries for each conventional generating unit node in the system, introducing a unit regulation coefficient, and establishing a functional relationship between system frequency deviation and changes in conventional generating unit output, which is used to simulate the dynamic regulation capacity of conventional power sources when the grid frequency fluctuates after the access of new energy sources.

[0087] In another alternative implementation, establishing a power grid model may also include constructing a load-based model that considers user response behavior, dividing the load into three categories: shiftable loads, transferable loads, and loads that can be reduced, setting their respective scheduling intervals, minimum / maximum power, and control variable states, and dynamically adjusting the load-side response behavior during capacity assessment to improve the overall coordination capability of the system.

[0088] This invention integrates renewable energy output models, conventional unit regulation models, and multi-type load behavior models into the power grid model, which can realistically reflect the power injection and regulation capabilities of the power grid under complex operating conditions. It provides comprehensive, dynamic, and detailed modeling support for subsequent power flow calculations and capacity assessments, and significantly improves the accuracy and reliability of renewable energy open capacity assessment results.

[0089] In the embodiments of this application, step S2 quantifies the relevant constraints for calculating the open capacity of the power grid, taking into account power grid security.

[0090] The quantitative power grid security constraints include boundary limits describing voltage, current, frequency, and transmission equipment capacity, which serve as constraint benchmarks for determining the feasibility of opening up capacity.

[0091] The constraints include the upper and lower limits of node voltage and the voltage constraints at the beginning and end of branches, the maximum transmission capacity constraints of transmission lines and transformers, branch current constraints, and grid frequency constraints.

[0092] Specifically, after new energy sources are connected, the voltage must be maintained within the allowable range. Excessively high or low voltage will affect the safety of the power grid operation and the stability of equipment. Node voltage upper and lower limits and branch start and end voltage constraints are required.

[0093]

[0094] in, The squared voltage values ​​at nodes i and j are V, respectively. j,max V j,min These are the upper and lower limits of the voltage at node j, respectively, p ij q ij Let z be the magnitudes of the active and reactive power flows through branch (i, j), respectively, where M is a sufficiently large positive number. ij r is a 0-1 state variable representing the on / off state of branch (i, j).ij x ij Let be the resistance and reactance of branch (i, j), respectively; B be the set of nodes in the network; and E be the set of branches in the network.

[0095] Power grid equipment such as transmission lines and transformers have maximum transmission capacity limitations. It is essential to ensure that, under worst-case conditions (such as maximum load or after a line fault), the load on any transmission line does not exceed its transmission capacity. The power flow constraints for any node j are as follows:

[0096]

[0097] Among them, P ij,t and Q ij,t Let V(j) be the active and reactive power flow of branch ij at time t, V(j) be the set of downstream nodes of node j, T be the set of time points, t∈T, T={1,2,…,24}, and P be the active and reactive power flow of branch ij at time t. j,t and Q j,t These represent the injected active and reactive power at node j, respectively. and These represent the conventional load, wind power, and solar power output of node j, respectively. This represents the maximum active power supply capacity of the power grid. Let be the openable capacity of node j; The power factor angle; i ij,t Let be the square of the branch current ij at time t.

[0098] Branch current constraints are:

[0099]

[0100] I xz >I m

[0101] Among them, I ij,max I ij,min Let be the maximum and minimum values ​​of the current in branch (i, j), respectively. Let I be the square of the current at branch (i, j). xz For the system bus short-circuit current, I m Permissible short-circuit current value

[0102] The integration of new energy sources can affect the frequency regulation capability of the power grid. It is essential to ensure that the grid frequency fluctuates within an acceptable range to prevent excessive frequency deviations from causing system instability. To guarantee system frequency security, after the integration of new energy sources, any rise or fall in frequency must be within the system's tolerable safe range. Therefore, the following grid frequency constraints can be established:

[0103] f b ≥f N +fN Δf max ≥f a

[0104] Among them, f a f is the lower limit of the frequency. b f is the upper limit of the frequency. N Let Δf be the system fundamental frequency. max This represents the maximum frequency deviation.

[0105] In an optional implementation, the constraints for calculating the open capacity of the power grid, which take into account grid security, may further include: introducing restrictions on the maximum transmission capacity of transmission lines and transformers, branch currents, and frequency deviations; wherein, the power transmission constraints of transmission equipment are matched and verified by injecting active and reactive power into the power flow with the upper limit of capacity; the branch current constraints are determined by comparing the square value of the current with the square of the maximum allowable current to determine whether the limit is exceeded; the frequency constraints are limited by setting the upper and lower limits of the system frequency and the tolerance range of the reference frequency fluctuation, so as to avoid frequency instability caused by disturbances from new energy output.

[0106] In another optional implementation, the quantitative consideration of the open capacity calculation of the power grid for power grid security may also include: based on the above-mentioned multiple constraints, constructing a transmission power limit judgment index for the post-fault operation state for the N-1 fault case, obtaining the branch transmission power based on the power flow calculation results after disconnecting the generator or branch and comparing it with its safety limit value to construct a limit exceedance rate index, which is used to characterize the safety margin of system operation under N-1 disturbance.

[0107] This invention uses a unified quantitative modeling of safety constraints for voltage, current, capacity, frequency, and N-1 fault scenarios to comprehensively cover various grid operation boundary problems that may be caused by the grid connection of new energy sources. It effectively improves the adaptability, accuracy, and engineering availability of capacity assessment under different operating scenarios, and provides a reliable capacity reference for the planning and scheduling of new energy grid connection.

[0108] In the implementation of this application, in step S3, zero open capacity is selected as the initial value. The Markov chain Monte Carlo method is used to simulate the cross-sectional operation data of the new energy unit. Combined with the established power grid model, power flow calculation is performed to determine whether the power flow, voltage, current and frequency of the power grid calculated under the open capacity meet the constraints, and further to determine whether the N-1 fault safety analysis is met.

[0109] The Markov chain Monte Carlo method is used to simulate the cross-sectional operation data of new energy units. This includes constructing a state transition matrix based on historical operation data of new energy units and generating a typical power sequence that fits the uncertainty of actual output using random sampling.

[0110] The Markov chain Monte Carlo method is used to simulate the cross-sectional operation data of new energy units. Combined with the established power grid model, power flow calculations are performed to obtain the power flow, voltage, current, and frequency parameters of the power grid under the current available capacity. The calculated node voltage and branch voltage difference, transmission capacity of transmission lines and transformers, branch current, and frequency fluctuation values ​​are compared with the constraints to determine whether the calculated power flow, voltage, current, and frequency under the available capacity meet the constraints. If all constraints are met, N-1 fault safety analysis is performed, including disconnecting any branch or generator and recalculating the power flow. If no islanding structure is formed, the transmission power of the branch under the fault condition is evaluated to determine whether there is a risk of exceeding the limit.

[0111] Specifically, by utilizing the Markov property of the MCMC method (i.e., the state at the next time step depends only on the current state and is independent of states at other time steps), it is possible to construct a typical power generation characteristic curve that incorporates both the uncertainty of new energy output and can fit with historical output scenarios, such as... Figure 4 The diagram shows the simulation flowchart of the new energy output curve based on MCMC.

[0112] Discrete state generation assigns each power value of the renewable energy power generation output (sampled per unit time) to a specific state and corresponds it to a series of discretized states in a Markov chain. Assume that the output range of a certain renewable energy power plant per unit time is (P... m,i,max P m,i,min If the discrete state data is N, then the output interval h covered by each state is... m,i for:

[0113]

[0114] The output P of new energy sources at a certain moment within this interval (t) The corresponding discrete state x (x∈{1,2,···,N}) is:

[0115] x = [P] (t) / h m,i ]

[0116] The state transition process is represented by a state transition matrix P (which remains unchanged during the transition process), which shows the transition probabilities between each state. The matrix element estimation is shown in the following formula:

[0117]

[0118] Among them, P ij P represents the probability that state i transitions to state j at time n-1; r x represents a probability function; (n) and x (n -1)These represent the states at times n and n-1, respectively; n ij Let be the number of transitions from process i to j.

[0119] Calculate the cumulative probability distribution matrix P using the state transition matrix P. cum :

[0120]

[0121] The Monte Carlo sampling process involves determining the initial state i. For wind farms, state i can be randomly selected; for photovoltaic power plants, the generated time series has intervals (no-sunlight periods are not simulated). To achieve more accurate simulation, the sunrise and sunset times are used as the start and end times of the daily simulation series, and the initial state for each day is sampled from the marginal distribution of power generation during the initial period. This marginal distribution can be approximated by statistical analysis of historical data, as shown in the following formula:

[0122]

[0123] Where, n j This represents the number of times a data point is in state j in the historical data.

[0124] Sample the next state from the current state i. Generate a random number u from a uniform distribution and then combine u with matrix P. cum Compare the i-th row. If p cum,i(j-1) <u≤p cum,ij If j is selected as the state at the next moment, then the state is converted into the corresponding power generation according to the following formula:

[0125] P = P j,min +u(P j,max -P j,min )

[0126] Where P is the analog power at that moment; P j,min With P j,max Let $\mathbf{j}$ be the lower and upper limits of the power range covered by state $j$.

[0127] This invention's N-1 fault safety analysis mainly focuses on two aspects: disconnecting a generator and disconnecting a branch circuit. It uses transmission power as an over-limit indicator to assess fault risk.

[0128] The present invention defines the following formula for calculating the transmission power limit violation rate:

[0129] P e =(P ij -P lim ) / P lim ×100%

[0130] Among them, P ijFor branch transmission power, P lim This represents the safe limit for the power transmitted through the branch circuit.

[0131] Figure 5 The flowchart illustrates the steps involved in N-1 security analysis. The steps of N-1 security analysis are as follows:

[0132] S101. When N=1 in the power grid.

[0133] S102. Disconnect transmission branch N (generator) and perform power flow calculation.

[0134] S103. If disconnecting a branch results in the system being divided into two unconnected systems, creating an island, then the island needs to be identified and processed.

[0135] S104. Determine if there is a power limit violation at this time. If yes, record the limit violation situation. At this time, it is not advisable to open a large capacity, and restore the disconnected branch (generator); if no, proceed to step S104.

[0136] S105. Restore the disconnected branch (generator).

[0137] S106. Let N = N+1. If N > the total number of transmission branches (generators) T, then the N-1 safety analysis is complete; otherwise, proceed to S102.

[0138] In the implementation of this application, if the constraint conditions are met in step S4, the available capacity is increased by an incremental step using a trial method, and the constraint conditions are repeatedly verified and N-1 fault safety judgment is performed for each trial capacity value. If the constraint conditions are not met, the binary method is used to backtrack and calculate until all constraint conditions are met, and the available capacity of new energy is obtained.

[0139] The trial method is adopted to gradually increase the available capacity according to the preset step size. For each trial capacity value, new energy operation data simulation, power flow calculation, constraint condition verification and N-1 fault safety judgment are carried out until a certain available capacity value fails to meet the constraint condition for the first time, which is determined as the trial over-limit capacity.

[0140] When the trial-and-error method is used to incrementally increase the available capacity until a certain available capacity value no longer meets the constraints, the available capacity that does not meet the constraints is determined as the trial over-limit capacity, and the previously available capacity that met the constraints is recorded as the trial boundary capacity. Within the interval formed by the trial over-limit capacity and the trial boundary capacity, an intermediate capacity is selected as a new candidate capacity based on the bisection method. The Markov chain Monte Carlo method is used to simulate the cross-sectional operation data of the new energy units, and power flow calculation is performed in combination with the established power grid model to determine whether the power flow, voltage, current and frequency of the power grid under the candidate capacity meet the constraints. Further N-1 fault safety analysis is then performed. If all constraints are met, the intermediate capacity is used as the new boundary capacity to continue narrowing the interval. If not, the intermediate capacity is used as the new over-limit capacity to continue iterating until the available capacity interval meets the set accuracy, and the final available capacity of new energy that meets all constraints is output.

[0141] Specifically, if the constraints are met, the available capacity is increased incrementally using a trial-and-error method, and step S3 is repeated. If the constraints are not met, the available new energy capacity is calculated using a bisection method. Figure 6 The flowchart shows a method for quickly calculating the available capacity of new energy sources using the binary search approach.

[0142] The available capacity is increased incrementally using a trial-and-error method. The expression for the incremental available capacity is as follows:

[0143] S n+1 =ΔS+S n

[0144] S0 = 0

[0145] Among them, S n To test the openable capacity n times, S n+1 Let Sn+1 be the openable capacity, ΔS be the increment step size of each trial, and S0 be the initially selected openable capacity value.

[0146] The trial open capacity continues until it fails to meet grid constraints. At this point, the trial open capacity is recorded as the trial over-limit capacity. Then, the renewable energy open capacity is calculated using the bisection method. The formula for calculating the renewable energy open capacity using the bisection method is:

[0147] S kmax =S Z =(2S) yx -ΔS) / 2

[0148] Among them, S kmax For maximum open capacity, S Z For intermediate capacity, S yx To test the capacity limits.

[0149] Example 3, referring to Figure 7 This is one embodiment of the present invention, which provides a rapid assessment system for the open capacity of new energy sources that takes into account grid security constraints, including a grid modeling module, a constraint condition module, a simulation judgment module, and a capacity iteration module.

[0150] The power grid modeling module is used to build power grid models, including new energy unit models, conventional unit models, load foundation models, line models, and transformer models.

[0151] The constraint module is used to quantify the constraints related to the calculation of the open capacity of the power grid, taking into account grid security.

[0152] The simulation judgment module is used to select zero open capacity as the initial value, and uses the Markov chain Monte Carlo method to simulate the cross-sectional operation data of the new energy unit model. Combined with the established power grid model, it performs power flow calculation, judges whether the calculated power flow, voltage, current and frequency of the power grid under the open capacity meet the constraints, and further judges whether it meets the N-1 fault safety analysis.

[0153] The capacity iteration module is used to increase the available capacity in incremental steps using a trial method if the constraints are met. For each trial capacity value, the constraints are repeatedly verified and N-1 fault safety judgment is performed. If the constraints are not met, the binary search method is used to backtrack and calculate until all constraints are met, thus obtaining the available capacity of new energy.

[0154] This embodiment also provides an electronic device applicable to a method for rapidly assessing the open capacity of new energy sources that takes into account grid security constraints, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for rapidly assessing the open capacity of new energy sources that takes into account grid security constraints as proposed in the above embodiment.

[0155] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a rapid assessment method for the open capacity of new energy sources that takes into account grid security constraints, as proposed in the above embodiments.

[0156] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for rapid assessment of open capacity of new energy sources that takes into account grid security constraints proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0157] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0158] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A rapid assessment method for the open capacity of new energy sources taking into account grid security constraints, characterized in that: include, Establish a power grid model, including a new energy unit model, a conventional unit model, a load base model, a line model, and a transformer model; Quantitatively consider the constraints related to calculating the open capacity of the power grid, taking into account power grid security. Using zero open capacity as the initial value, the Markov chain Monte Carlo method is used to simulate the cross-sectional operation data of the new energy unit model. Combined with the established power grid model, power flow calculation is performed to determine whether the calculated power flow, voltage, current, and frequency under the open capacity meet the constraints, and further to determine whether the N-1 fault safety analysis is satisfied. If the constraints are met, the trial method is used to increase the available capacity in incremental steps, and the constraint verification and N-1 fault safety judgment are repeated for each trial capacity value. If the constraints are not met, the binary search method is used to backtrack and calculate until all constraints are met, and the available capacity of new energy is obtained.

2. The rapid assessment method for the open capacity of new energy sources taking into account grid security constraints as described in claim 1, characterized in that: The establishment of the power grid model includes constructing multiple types of parameter models that characterize the output characteristics of new energy sources, conventional regulation capabilities, load dispatch characteristics, and electrical topology, supporting power flow calculation and constraint verification.

3. The rapid assessment method for the open capacity of new energy sources taking into account grid security constraints as described in claim 2, characterized in that: The quantitative power grid security constraints include boundary limits describing voltage, current, frequency, and transmission equipment capacity, which serve as constraint benchmarks for determining the feasibility of open capacity.

4. The rapid assessment method for the open capacity of new energy sources taking into account grid security constraints as described in claim 3, characterized in that: The simulation of the cross-sectional operation data of the new energy unit model using the Markov chain Monte Carlo method includes constructing a state transition matrix based on the historical operation data of the new energy unit and generating a typical power sequence that fits the uncertainty of the actual output using random sampling.

5. The rapid assessment method for the open capacity of new energy sources taking into account grid security constraints as described in claim 4, characterized in that: The simulation of the cross-sectional operation data of the new energy unit model using the Markov chain Monte Carlo method includes, The Markov chain Monte Carlo method is used to simulate the cross-sectional operation data of the new energy unit model. Combined with the established power grid model, power flow calculation is performed to obtain the power flow, voltage, current and frequency parameters of the power grid under the current available capacity. The calculated node voltage and branch voltage difference, transmission capacity of transmission lines and transformers, branch current, and frequency fluctuation value are compared with the constraint conditions to determine whether the power flow, voltage, current, and frequency of the power grid under the open capacity meet the constraint conditions. If all constraints are satisfied, the N-1 fault safety analysis continues, including recalculating the power flow after disconnecting any branch or generator. If no island structure is formed, the transmission power of the branch under fault conditions is evaluated to determine whether there is a risk of exceeding the limit. The constraints include node voltage upper and lower limits and branch start and end voltage constraints, maximum transmission capacity constraints of transmission lines and transformers, branch current constraints, and grid frequency constraints.

6. The rapid assessment method for the open capacity of new energy sources taking into account grid security constraints as described in claim 5, characterized in that: The method of increasing the available capacity using a trial-and-error approach in incremental steps includes: The trial method is adopted to gradually increase the available capacity according to the preset step size. For each trial capacity value, new energy operation data simulation, power flow calculation, constraint condition verification and N-1 fault safety judgment are carried out until a certain available capacity value fails to meet the constraint condition for the first time, which is determined as the trial over-limit capacity.

7. The rapid assessment method for the open capacity of new energy sources taking into account grid security constraints as described in claim 6, characterized in that: The binary search method is used to backtrack until all constraints are met, including... When the openable capacity is increased incrementally using the trial method until a certain openable capacity value does not meet the constraints, the openable capacity that does not meet the constraints is determined as the trial over-limit capacity, and the openable capacity value that met the constraints last time is recorded as the trial boundary capacity. Within the interval formed by the trial over-limit capacity and the trial boundary capacity, the intermediate capacity is selected as a new candidate capacity based on the bisection method. The Markov chain Monte Carlo method is used to simulate the cross-sectional operation data of the new energy unit model. Combined with the established power grid model, power flow calculation is performed to determine whether the power flow, voltage, current and frequency of the power grid under the candidate capacity meet the constraints. Further N-1 fault safety analysis is then performed. If all constraints are met, the intermediate capacity is used as the new boundary capacity to continue narrowing the range. If not, the intermediate capacity is used as the new over-limit capacity to continue iterating until the open capacity range meets the set precision, and the final open capacity of new energy that meets all constraints is output.

8. A rapid assessment system for the openable capacity of new energy sources taking into account grid security constraints, employing the rapid assessment method for the openable capacity of new energy sources taking into account grid security constraints as described in any one of claims 1 to 7, characterized in that, include: The module includes a power grid modeling module, a constraint condition module, a simulation judgment module, and a capacity iteration module. The power grid modeling module is used to build power grid models, including new energy unit models, conventional unit models, load foundation models, line models, and transformer models. The constraint module is used to quantify the relevant constraints for calculating the open capacity of the power grid considering power grid security. The simulation judgment module is used to select zero open capacity as the initial value, use the Markov chain Monte Carlo method to simulate the cross-sectional operation data of the new energy unit model, combine the established power grid model to perform power flow calculation, and judge whether the calculated power flow, voltage, current and frequency of the power grid under the open capacity meet the constraints, and further judge whether the N-1 fault safety analysis is met. The capacity iteration module is used to increase the available capacity in incremental steps using a trial method if the constraints are met, and to repeatedly verify the constraints and perform N-1 fault safety judgment for each trial capacity value. If the constraints are not met, a binary search method is used to backtrack and calculate until all constraints are met, thus obtaining the available capacity of new energy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the rapid assessment method for the open capacity of new energy sources taking into account grid security constraints, as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the rapid assessment method for the open capacity of new energy sources taking into account grid security constraints, as described in any one of claims 1 to 7.

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