Method for measuring and calculating new energy bearing capacity of power grid

Abstract

The invention discloses a power grid new energy bearing capacity measurement and calculation method, and relates to the technical field of power grid bearing capacity measurement and calculation, and the method comprises the following steps: constructing a power grid dynamic model comprising a power generation unit, a power transmission and transformation network, a load node and a new energy access point; establishing a basic safety margin boundary, and generating a static power flow model; constructing a dynamic stability model reflecting the influence of new energy fluctuation on power grid frequency and voltage dynamic response by using a dynamic simulation platform; and dynamic stability analysis: new energy inverter access points are added in the power grid dynamic model to construct a dynamic system model, a virtual inertia control module is embedded in the new energy inverter model, and a control loop is constructed. According to the method, a final control instruction is generated by constructing a static model and a dynamic model, embedding a virtual inertia control module in an inverter model, constructing a control loop and combining virtual inertia compensation power with inverter original power.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid carrying capacity calculation, and specifically relates to a method for calculating the new energy carrying capacity of a power grid. Background Art

[0002] The "grid's renewable energy carrying capacity" refers to the maximum capacity of the grid to accommodate and consume renewable energy (such as wind power, photovoltaics, and distributed generation) while maintaining safe, stable, and efficient operation. The intermittent and volatile nature of renewable energy output during large-scale grid integration places higher demands on voltage levels, line loads, and dynamic stability. Therefore, accurately measuring the grid's renewable energy carrying capacity is crucial for grid planning, operational control, and security assurance.

[0003] Announcement No. CN111211576A discloses a method for calculating the new energy carrying capacity of a regional power grid taking into account energy storage. This method comprehensively considers the load level, the power generation capacity of conventional energy, the configuration capacity of energy storage and the transmission limit of the partitioned section, and uses the regional power grid stable operation margin, the conventional support energy stable operation margin, and the regional power grid power balance margin as indicators for calculating the regional power grid's new energy carrying capacity. This method can provide more practical and reliable guidance for the scientific formulation of new energy scheduling and planning decisions in power grid operation, facilitate operators to control the regional power grid's new energy carrying capacity, and provide a decision-making basis for the planning and construction of the power grid structure and power supply structure.

[0004] The volatility of new energy sources is mainly reflected in the large changes in their short-term output, and this change will instantly have a significant impact on the safe and stable operation of the power grid. Traditional large-scale thermal power and nuclear power generators have high inertia due to their own rotating machinery. When the load or power generation suddenly changes, they can help maintain the stability of the power grid frequency through inertial response in a short period of time. In contrast, new energy sources such as wind power and photovoltaics are mainly connected to the power grid through inverters. They naturally lack physical inertia, which makes the power grid system prone to frequency deviation and large oscillations when facing new energy fluctuations. When the output of new energy suddenly drops or rises, if there is not enough inertia or fast-responding regulation resources involved, the frequency of the system may quickly deviate from the rated value, thereby triggering protection actions or further causing system instability. To this end, this application proposes a method for calculating the carrying capacity of new energy in the power grid. This method adds virtual inertia control technology to the traditional measurement model to simulate the inertial response of traditional generator sets, thereby effectively responding to the sharp fluctuations in the output of new energy. Summary of the Invention

[0005] In response to the above technical problems existing in the prior art, the present invention proposes a method for calculating the new energy carrying capacity of a power grid, which has a reasonable design, overcomes the shortcomings of the prior art, and has good results.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for calculating the new energy carrying capacity of a power grid comprises the following steps:

[0008] Step S1: Collect and process grid operation data to construct a grid dynamic model including power generation units, transmission and transformation networks, load nodes, and new energy access points;

[0009] Step S2: Construction of static power flow model and dynamic stability model;

[0010] Establish a static power flow model to determine the safety margin boundary; use the dynamic simulation platform to build a dynamic stability model to reflect the dynamic response of renewable energy fluctuations to grid frequency and voltage;

[0011] Step S3: static power flow analysis;

[0012] Conduct static power flow analysis without renewable energy access to determine the voltage, line load, and equipment operating limits of each node. Perform power flow calculations under design scenarios to assess the impact of renewable energy access on grid stability indicators.

[0013] Step S4: dynamic stability analysis;

[0014] Adding inverter access points to the grid dynamic model creates a dynamic system model. A virtual inertia control module is embedded in the inverter model to create a closed-loop control loop. The virtual inertia compensation power is calculated based on the real-time grid frequency change rate and used as compensation for the inverter output to simulate the inertia effect.

[0015] Step S5: With supply capacity as the horizontal axis and regulation capacity as the vertical axis, a two-dimensional evaluation model is constructed. Based on the simulation results of the evaluation model, safety margin, redundancy rate, and risk index indicators are defined. Safety zones, margin zones, and risk zones are divided, and the maximum proportion of new energy that the power grid can accept under each scenario is clarified.

[0016] Preferably, the static power flow model construction includes the following steps:

[0017] Step S2.1.1: Establish the power balance equation of each node in the power grid;

[0018] Step S2.1.2: Use numerical methods to solve the power balance equation to obtain the voltage distribution of each node and the line power flow;

[0019] Step S2.1.3: Determine the steady-state operating range of the power grid based on the solution results, eliminate the safety margin boundary requirements, and define the safety margin index;

[0020] Step S2.1.4: Output the results of each node voltage, line flow and power balance state to form a static power flow model.

[0021] Preferably, the dynamic simulation steps of the dynamic stability model are as follows:

[0022] Step S2.2.1: Use the static power flow model to obtain the initial voltage, phase angle, and branch load of each node, initialize the rotor angle and frequency of each synchronous machine, and the power injection values ​​of each load and new energy source;

[0023] Step S2.2.2: Establish a swing equation for each synchronous machine, use the network node power balance equation to determine the real-time voltage and phase angle changes of each node during the disturbance, and superimpose the load and new energy disturbance as input terms into the power balance equation;

[0024] Step S2.2.3: Set disturbance events to drive the power system dynamic response;

[0025] Step S2.2.4: Perform simulations using numerical algorithms to observe the responses of the power system frequency and rotor angle voltage over time, analyze frequency deviation, frequency change rate, and oscillation decay time, and evaluate the stability of the power system.

[0026] Step S2.2.5: Compare the power system status before and after the disturbance, analyze the changes in node voltage and line load after the disturbance, and determine whether the static safety margin is exceeded.

[0027] Preferably, the synchronous machine swing equation is used to describe the generator dynamic response:

[0028]

[0029]

[0030] Among them, δ i is the rotor angle of the i-th unit, ω i is the speed of the i-th unit, ω0 is the rated synchronous angular velocity of the system, H i is the inertia constant of the unit, P m,i is the mechanical input power, P e,i is the electrical output power, D i is the damping coefficient, which reflects the natural suppression effect of the unit on frequency deviation.

[0031] Preferably, the power flow analysis and new energy access assessment are performed using a static power flow model, including the following steps:

[0032] Step S3.1: Add new energy generation units to the grid dynamic model and set different scenarios for new energy access ratios and power generation changes;

[0033] Step S3.2: Perform static power flow calculation on the power grid dynamic model again, redistribute the output and load analysis of the generator sets, and analyze the calculated voltage changes at each node and line load changes;

[0034] Step S3.3: Compare the power flow calculation results under different scenarios with the steady-state results to determine key indicators: voltage level, line load, power balance and equipment margin;

[0035] Step S3.4: Calculate the power flow calculation results under different scenarios and quantify the impact of renewable energy access on the grid system's carrying capacity.

[0036] Preferably, data statistics are collected for multiple renewable energy access ratios and output fluctuation scenarios to form a data table, and quantitative indicators of safety margin, redundancy rate and risk index are defined, where:

[0037] The safety margin is the margin value of the node voltage within the specified range or the ratio of the line load to the thermal limit;

[0038] The redundancy ratio is the ratio of the power system's reserve capacity to the actual output demand;

[0039] The risk index is a comprehensive risk level formed by combining multiple indicators.

[0040] Preferably, in step S4, the closed-loop control circuit is constructed as follows:

[0041] Step S4.1: Set frequency measurement points in the power grid dynamic model to collect system frequency data in real time, and use the sampled data in combination with the discrete difference method to calculate the frequency change rate;

[0042] Step S4.2: Arrange the control law and determine the virtual inertia constant based on the control law;

[0043] Step S4.3: Calculate the output virtual inertia compensation power using the virtual inertia constant and the real-time frequency change rate;

[0044] Step S4.4: The virtual inertia compensation power is used as the auxiliary compensation power and added to the original power setting of the inverter to form the final output control instruction;

[0045] Step S4.5: A closed-loop feedback structure is formed by real-time acquisition of grid frequency, calculation of frequency change rate, calculation of virtual inertia constant, calculation of virtual inertia compensation power, adjustment of inverter output power, and re-measurement of frequency change after the power system responds. The power system responds to disturbances based on the closed-loop feedback structure.

[0046] Preferably, the closed-loop control loop is simulated using a dynamic simulation platform to verify the following key indicators:

[0047] Frequency response: Obtain the system frequency drop and recovery process when a disturbance occurs, verify whether virtual inertia control can delay the frequency drop and the power system recovery speed;

[0048] Oscillation decay: Analyzes whether the power system has persistent oscillations after a disturbance and how quickly the power system oscillation decays to a steady state through virtual inertia control.

[0049] Comparative evaluation: Analyze the differences in system dynamic response with and without virtual inertia control, and quantify the improvement in power system stability achieved through control improvements.

[0050] Preferably, the horizontal axis in the two-dimensional evaluation model is the percentage of maximum accessible energy capacity or the steady-state safety margin value, and the vertical axis is the regulation index or dynamic response speed index based on frequency regulation capability and spare capacity. Flow and dynamic simulation calculations are performed on each scenario to obtain the indicator data of the corresponding scenario and generate two-dimensional coordinate points.

[0051] Preferably, quantify the impact of new energy access:

[0052] For different renewable energy access ratios and output fluctuation scenarios, supply indicators and regulation indicators are calculated separately, and each scenario point is calibrated in the two-dimensional model;

[0053] Based on the indicator data under all scenarios, the safety margin, redundancy rate and risk index of each scenario are calculated, and statistical methods are used to quantitatively describe the impact of renewable energy integration on the power system's carrying capacity;

[0054] Find the boundary points based on the distribution of each scenario and determine the maximum proportion of new energy that the system can stably accept under each scenario.

[0055] The beneficial technical effects brought about by the present invention are:

[0056] 1. This application constructs static and dynamic models to perform static power flow analysis and dynamic stability analysis on the power grid, embeds a virtual inertia control module in the inverter model, constructs a control loop, combines the virtual inertia compensation power with the original inverter power, and generates the final control instructions to simulate the inertial response and realize the measurement of the carrying capacity.

[0057] 2. By simulating the inertial response of traditional generator sets, the defect of new energy (such as wind power and photovoltaic power) lacking natural rotational inertia due to inverter access is compensated during the simulation process, thereby more realistically reproducing the inertial response provided by traditional synchronous units when facing sudden load changes or fluctuations in new energy output.

[0058] 3. Dynamic regulation can flexibly adjust inertia simulation according to on-site conditions, so that the system can respond quickly to sudden disturbances, stabilize the frequency, reduce virtual inertia when the power grid is running smoothly, reduce unnecessary power regulation burden, and avoid secondary oscillations.

[0059] 4. Closer to the inertial response of traditional synchronous machines, it reduces the impact of fluctuations in renewable energy output on grid security and ensures that the system maintains stable operation when a high proportion of renewable energy is connected. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 Flowchart of the calculation method of the present invention. DETAILED DESCRIPTION

[0061] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0062] The present invention provides a method for calculating the renewable energy carrying capacity of a power grid. Through virtual inertia control technology, the inertial response of a traditional generator set is simulated to ensure instantaneous power balance. During grid operation, the carrying capacity is calculated and the virtual inertia is dynamically adjusted according to whether renewable energy output decreases or increases.

[0063] like Figure 1 As shown, a method for calculating the new energy carrying capacity of a power grid includes the following steps:

[0064] S1. Collect historical grid operation data, load data, grid topology, equipment parameters, and new energy resource data, clean and normalize the data, and build a dynamic grid model that includes power generation units, transmission and transformation networks, load nodes, and new energy access points;

[0065] S2. Static and dynamic model construction to determine the voltage, line flow, and power balance status of each node, establish basic safety margin boundaries, and generate a static power flow model; use the dynamic simulation platform to build a dynamic stability model that reflects the impact of renewable energy fluctuations on the dynamic response of grid frequency and voltage;

[0066] S3. Static power flow analysis: Calculate the steady-state operation without renewable energy access, determine the voltage, line load, and equipment operating limits of each node, perform power flow calculations under the design scenario, and evaluate the impact of renewable energy access on grid voltage and line load indicators.

[0067] S4. Dynamic stability analysis: Add the new energy inverter access point to the grid dynamic model to build a dynamic system model. Embed the virtual inertia control module in the new energy inverter model to construct the following control loop:

[0068] The frequency change rate is calculated based on the real-time sampled grid frequency, and the virtual inertia constant is calculated based on the set control law. The virtual inertia compensation power is calculated based on the virtual inertia constant and the frequency change rate. The compensation power is used as compensation for the inverter output to simulate the inertia effect.

[0069] S5. Construct a two-dimensional assessment model with supply capacity as the horizontal axis and regulation capacity as the vertical axis. Define safety margin, redundancy rate, and risk index indicators based on the simulation results of the assessment model, and divide the model into safety zones, margin zones, and risk zones to clarify the maximum proportion of new energy that the power grid can accommodate under each scenario.

[0070] In one feasible approach, in traditional measurement models, the inherent rotational inertia of synchronous generator sets is relied upon to stabilize the grid frequency. The physical inertia of synchronous generator sets can quickly absorb or release energy in the grid system, buffering frequency fluctuations. When a high proportion of renewable energy is connected through the inverter, the inverter lacks sufficient physical inertia. Therefore, deviations will occur in the simulation of frequency response and dynamic stability, making it difficult to accurately reflect the actual response of the power system under severe fluctuations.

[0071] By measuring the grid system and its frequency and its changes, a virtual inertia reference information is calculated, and the active power output of the inverter is adjusted according to the signal. When the grid frequency drops, the inverter can quickly increase the output. Conversely, when the frequency rises, the output is quickly reduced, so that it can achieve the effect of inertia response of the synchronous unit.

[0072] Among them, the static power flow model provides initial conditions and boundary values ​​for the dynamic stability model.

[0073] Example 1: The static power flow model construction method is as follows:

[0074] S2.1.1. Establish the power balance equation for each node in the power grid;

[0075] S2.1.2. Use numerical methods to solve the power balance equation to obtain the voltage distribution at each node and the line power flow;

[0076] S2.1.3. Determine the steady-state operating range of the power grid based on the solution results, eliminate the safety margin boundary requirements, and define the safety margin index;

[0077] S2.1.4. Output the results of each node voltage, line flow and power balance status to form a static power flow model and provide initial conditions and boundary data for dynamic simulation.

[0078] In one possible implementation, the power grid is abstracted as a collection of nodes and lines. Nodes include generators, load centers, and renewable energy access points, while lines represent transmission lines and transformers. The power balance equation for node i is:

[0079]

[0080] Among them, P i Indicates the node active power, V i Indicates the voltage, G ik 、B ik are the real and imaginary parts of the line admittance, θ ik is the phase difference between nodes.

[0081] The Newton-Raphson iteration method is used to solve the equations, gradually approaching the true value to ensure accuracy. Finally, combined with dynamic models, a comprehensive assessment of grid stability is conducted to optimize renewable energy integration strategies.

[0082] Example 2: Dynamic stability model dynamic simulation steps are as follows:

[0083] S2.2.1. Use the static power flow model to obtain the initial voltage, phase angle, and branch load of each node, and initialize the rotor angle and frequency of each synchronous machine, as well as the power injection values ​​of each load and new energy source;

[0084] S2.2.2. Establish a swing equation for each synchronous machine, use the network node power balance equation to determine the real-time voltage and phase angle changes of each node during the disturbance, and superimpose the load and new energy disturbance as input terms into the power balance equation;

[0085] S2.2.3. Set the disturbance event of renewable energy output fluctuation caused by sudden change of load, wind speed or irradiance as the time series input, apply the disturbance instantaneously to the corresponding node, and drive the dynamic response of the power system;

[0086] S2.2.4. Use numerical algorithms to simulate and observe the time-varying responses of the power system frequency and rotor angle voltage, analyze frequency deviation, frequency change rate, and oscillation decay time, and evaluate the stability of the power system;

[0087] S2.2.5. Compare the power system status before and after the disturbance, analyze the changes in node voltage and line load after the disturbance, and determine whether they exceed the static safety margin limit.

[0088] In one feasible approach, without considering virtual inertia control, the dynamic stability model is based on the dynamic model of the synchronous machine, a detailed description of the power grid network, and load and renewable energy disturbance data to reflect the natural dynamic characteristics of the unit. The renewable energy fluctuation is input in the form of exogenous disturbances to simulate the transient response encountered by the power system in actual operation.

[0089] Among them, the dynamic stability model is used to evaluate the impact of the power grid on the dynamic stability of the system when a high proportion of new energy is connected, because new energy does not have rotational inertia, as well as the weak state of the power system when there is no additional virtual inertia compensation means.

[0090] Example 3: Synchronous machine swing equation is used to describe the dynamic response of the generator:

[0091]

[0092] Among them, δ i is the rotor angle of the i-th unit, ω i is the speed of the i-th unit, ω0 is the rated synchronous angular velocity of the system, H i is the inertia constant of the unit, P m,i is the mechanical input power, P e,i is the electrical output power, D i is the damping coefficient, which reflects the natural suppression effect of the unit on frequency deviation.

[0093] In one possible implementation, the first equation describes the rate of change of the rotor angle Determined by the difference between the actual speed and the synchronous speed.

[0094] The second equation reveals the rotor acceleration The net acceleration power P m,i -P e,i Subtract the damping loss D i (ω i -ω0), and the unit inertia reserve 2H i The ratio is determined.

[0095] The swing equation describes how generators in power systems respond to disturbances and how they recover to steady state. It is the basis for the stability analysis of power systems. In practical applications, by simulating the swing equation, we can evaluate the stability of the system to various disturbance scenarios (such as sudden load changes or new energy fluctuations) and provide a theoretical basis for the design of control strategies.

[0096] Example 4: Power flow analysis and new energy access assessment using a static power flow model:

[0097] S3.1. Add new energy generation units to the grid dynamic model and set different scenarios for new energy access ratios and power generation changes.

[0098] S3.2. Perform static power flow calculations again on the dynamic model of the power grid, redistribute the output and load analysis of the generator sets, and analyze the calculated voltage changes at each node and line load changes.

[0099] S3.3. Compare the power flow calculation results under different scenarios with the steady-state results to determine key indicators: voltage level, line load, power balance and equipment margin;

[0100] S3.4. Calculate the power flow calculation results under different scenarios and quantify the impact of new energy access on the carrying capacity of the power grid system.

[0101] Among the key indicators:

[0102] Voltage level: Check whether the voltage at each node remains within the safety margin and whether there is any local voltage that is too low or too high.

[0103] Line Loading: Evaluates changes in power flow on the line to determine if the line load factor exceeds the equipment's operating limits.

[0104] Power balance and equipment margin: Analyze whether each device has sufficient margin to cope with the impact of large fluctuations in renewable energy.

[0105] In one feasible approach, static power flow analysis serves as the basis for evaluating the steady-state operation of the power grid. Data collection, modeling, and power flow calculations are performed on the power grid without the access of new energy sources, thereby determining the operating limits of the voltage, line load, and equipment at each node. On this basis, a scenario is designed to incorporate new energy power generation units, and power flow calculations are performed again to evaluate the specific impact of new energy access on voltage levels and line loads, revealing whether each part of the equipment is still within the safe operating range after the access of new energy, thereby providing initial conditions and a verification basis for dynamic simulation.

[0106] Example 5: Data statistics are collected for multiple renewable energy access ratios and output fluctuation scenarios to form a data table, and quantitative indicators such as safety margin, redundancy rate, and risk index are defined, where:

[0107] The safety margin is the margin value of the node voltage within the specified range or the ratio of the line load to the thermal limit;

[0108] The redundancy ratio is the ratio of the power system's reserve capacity to the actual output demand;

[0109] The risk index is a comprehensive risk level formed by combining multiple indicators.

[0110] In one feasible approach, by analyzing the statistical distribution of quantitative indicators and comparing the changing trends of different scenarios, it is used to identify the safety hazards and weak links in the power system when the proportion of new energy access increases, provide quantitative decision-making support for grid planning and operation, and clarify the impact of new energy access on the grid's carrying capacity.

[0111] Among them, the use of static power flow models to comprehensively evaluate the new energy access scenarios has the following advantages: simulating different access ratios and power generation fluctuations under design scenarios, and reflecting the impact of new energy changes on the global power balance and equipment load.

[0112] By redistributing the output and load of generator sets, system balance can be achieved and risks of voltage and line exceeding limits can be detected.

[0113] Compare the calculation results with the baseline steady-state situation to identify changes in key indicators and provide data basis for the subsequent formulation of regulatory measures.

[0114] Using statistical and quantitative methods, a risk assessment system is established to clarify the impact of new energy access on the grid's carrying capacity.

[0115] Example 6: Control loop construction method:

[0116] S4.1. Set frequency measurement points in the power grid dynamic model to collect system frequency data in real time, and use the sampled data in combination with the discrete difference method to calculate the frequency change rate;

[0117] S4.2. Arrange the control law and determine the virtual inertia constant based on the control law;

[0118] S4.3. Calculate the output virtual inertia compensation power using the virtual inertia constant and the real-time frequency change rate. This power is used to generate additional active power supplemented by the virtual inertia according to the frequency change during disturbances.

[0119] S4.4. The virtual inertia compensation power is used as the auxiliary compensation power and added to the original power setting of the inverter to form the final output control instruction;

[0120] S4.5. A closed-loop feedback structure is formed by: real-time acquisition of grid frequency → calculation of frequency change rate → calculation of virtual inertia constant → calculation of virtual inertia compensation power → adjustment of inverter output power → re-measurement of frequency change after power system response. The power system responds to disturbances based on the closed-loop feedback structure.

[0121] Embodiment 7: A saturation limit of the output power is also embedded in the control loop to ensure that the virtual inertia compensation power does not exceed the maximum capacity of the inverter, thereby avoiding secondary disturbances caused by overcompensation.

[0122] The frequency change rate is In order to dynamically adjust the virtual inertia constant H v , preset a control law.

[0123]

[0124] Among them, H0 is the basic virtual inertia constant, k is the adjustment gain, which is a positive number, Δf r As the reference value, When the reference value is exceeded, it indicates that the disturbance is large and the virtual inertia needs to be increased. At the same time, upper and lower limits can be set to prevent the parameters from being too large or too small. The control law enables the inverter to automatically improve its "inertia" compensation capability when subjected to significant frequency changes to better imitate the inertia response of the synchronous unit.

[0125] Example 8: The control loop is simulated using a dynamic simulation platform to verify the following key indicators:

[0126] Frequency response: Obtain the system frequency drop and recovery process when a disturbance occurs, and verify whether virtual inertia control can delay the frequency drop and the power system recovery speed.

[0127] Oscillation attenuation: Analyzes whether the power system has persistent oscillations after a disturbance, and the speed at which the power system oscillations decay to a steady state through virtual inertia control.

[0128] Comparative evaluation: Analyze the differences in system dynamic response with and without virtual inertia control, and quantify the improvement in power system stability achieved through control improvements.

[0129] Example 9: In the two-dimensional assessment model, the horizontal axis is the percentage of maximum accessible energy capacity or the steady-state safety margin value, and the vertical axis is the regulation index or dynamic response speed index based on frequency regulation capability and spare capacity. Flow and dynamic simulation calculations are performed on each scenario to obtain the indicator data for the corresponding scenario and generate two-dimensional coordinate points.

[0130] Example 10: Quantifying the impact of new energy access:

[0131] For different renewable energy access ratios and output fluctuation scenarios, supply indicators and regulation indicators are calculated separately, and each scenario point is calibrated in the two-dimensional model;

[0132] Based on the indicator data under all scenarios, the safety margin, redundancy rate and risk index of each scenario are calculated, and statistical methods are used to quantitatively describe the impact of renewable energy integration on the power system's carrying capacity;

[0133] Find the boundary points based on the distribution of each scenario and determine the maximum proportion of new energy that the system can stably accept under each scenario.

[0134] In one feasible method, the boundary point is the critical point from the safety zone to the margin zone or the boundary from the margin zone to the risk zone. For example, when the coordinate point in the two-dimensional model just slides from the safety zone into the margin zone, it can be regarded as the remaining margin of the power grid to maintain safe operation is the lowest. The proportion of new energy in this state is the upper limit of the system's carrying capacity.

[0135] When the system operating point is in the safe zone, the power grid has greater room to accept new energy;

[0136] In the margin zone, the system operation is relatively tight and new energy needs to be connected with caution;

[0137] If it enters the risk zone, it means that the access to new energy has exceeded the system's carrying capacity, which may cause unstable operation or failure.

[0138] Through this method, the maximum proportion of renewable energy that the power grid can accept under different scenarios can be determined with the support of quantitative analysis.

[0139] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the scope of protection of the present invention.

Claims

1. A method for calculating the new energy carrying capacity of a power grid, characterized in that: The following steps are involved: Step S1: Collect and process grid operation data to construct a grid dynamic model including power generation units, transmission and transformation networks, load nodes, and new energy access points; Step S2: Construction of static power flow model and dynamic stability model; Establish a static power flow model to determine the safety margin boundary; use the dynamic simulation platform to build a dynamic stability model to reflect the dynamic response of renewable energy fluctuations to grid frequency and voltage; Step S3: static power flow analysis; Conduct static power flow analysis without renewable energy access to determine the voltage, line load, and equipment operating limits of each node. Perform power flow calculations under design scenarios to assess the impact of renewable energy access on grid stability indicators. Step S4: dynamic stability analysis; Adding inverter access points to the grid dynamic model creates a dynamic system model. A virtual inertia control module is embedded in the inverter model to create a closed-loop control loop. The virtual inertia compensation power is calculated based on the real-time grid frequency change rate and used as compensation for the inverter output to simulate the inertia effect. Step S5: With supply capacity as the horizontal axis and regulation capacity as the vertical axis, a two-dimensional evaluation model is constructed. Based on the simulation results of the evaluation model, safety margin, redundancy rate, and risk index indicators are defined. Safety zones, margin zones, and risk zones are divided, and the maximum proportion of new energy that the power grid can accept under each scenario is clarified.

2. The method for calculating the new energy carrying capacity of a power grid according to claim 1, characterized in that: The construction of the static power flow model includes the following steps: Step S2.1.1: Establish the power balance equation of each node in the power grid; Step S2.1.2: Use numerical methods to solve the power balance equation to obtain the voltage distribution of each node and the line power flow; Step S2.1.3: Determine the steady-state operating range of the power grid based on the solution results, eliminate the safety margin boundary requirements, and define the safety margin index; Step S2.1.4: Output the results of each node voltage, line flow and power balance state to form a static power flow model.

3. The method for calculating the new energy carrying capacity of a power grid according to claim 2, characterized in that: The dynamic simulation steps of the dynamic stability model are as follows: Step S2.2.1: Use the static power flow model to obtain the initial voltage, phase angle, and branch load of each node, initialize the rotor angle and frequency of each synchronous machine, and the power injection values ​​of each load and new energy source; Step S2.2.2: Establish a swing equation for each synchronous machine, use the network node power balance equation to determine the real-time voltage and phase angle changes of each node during the disturbance, and superimpose the load and new energy disturbance as input terms into the power balance equation; Step S2.2.3: Set disturbance events to drive the power system dynamic response; Step S2.2.4: Perform simulations using numerical algorithms to observe the responses of the power system frequency and rotor angle voltage over time, analyze frequency deviation, frequency change rate, and oscillation decay time, and evaluate the stability of the power system. Step S2.2.5: Compare the power system status before and after the disturbance, analyze the changes in node voltage and line load after the disturbance, and determine whether the static safety margin is exceeded.

4. The method for calculating the new energy carrying capacity of a power grid according to claim 3, characterized in that: The synchronous machine swing equation is used to describe the dynamic response of the generator: Among them, δ i is the rotor angle of the i-th unit, ω i is the speed of the i-th unit, ω0 is the rated synchronous angular velocity of the system, H i is the inertia constant of the unit, P m,i is the mechanical input power, P e,i is the electrical output power, D i is the damping coefficient, which reflects the natural suppression effect of the unit on frequency deviation.

5. The method for calculating the new energy carrying capacity of a power grid according to claim 4, characterized in that: The static power flow model is used to perform power flow analysis and new energy access assessment, including the following steps: Step S3.1: Add new energy generation units to the grid dynamic model and set different scenarios for new energy access ratios and power generation changes; Step S3.2: Perform static power flow calculation on the power grid dynamic model again, redistribute the output and load analysis of the generator sets, and analyze the calculated voltage changes at each node and line load changes; Step S3.3: Compare the power flow calculation results under different scenarios with the steady-state results to determine key indicators: voltage level, line load, power balance and equipment margin; Step S3.4: Calculate the power flow calculation results under different scenarios and quantify the impact of renewable energy access on the grid system's carrying capacity.

6. The method for calculating the new energy carrying capacity of a power grid according to claim 5, characterized in that: Collect data on multiple renewable energy access ratios and output fluctuation scenarios, generate a data table, and define quantitative indicators for safety margin, redundancy rate, and risk index, including: The safety margin is the margin value of the node voltage within the specified range or the ratio of the line load to the thermal limit; The redundancy ratio is the ratio of the power system's reserve capacity to the actual output demand; The risk index is a comprehensive risk level formed by combining multiple indicators.

7. The method for calculating the new energy carrying capacity of a power grid according to claim 6, characterized in that: In step S4, the closed-loop control circuit is constructed as follows: Step S4.1: Set frequency measurement points in the power grid dynamic model to collect system frequency data in real time, and use the sampled data in combination with the discrete difference method to calculate the frequency change rate; Step S4.2: Arrange the control law and determine the virtual inertia constant based on the control law; Step S4.3: Calculate the output virtual inertia compensation power using the virtual inertia constant and the real-time frequency change rate; Step S4.4: The virtual inertia compensation power is used as the auxiliary compensation power and added to the original power setting of the inverter to form the final output control instruction; Step S4.5: A closed-loop feedback structure is formed by real-time acquisition of grid frequency, calculation of frequency change rate, calculation of virtual inertia constant, calculation of virtual inertia compensation power, adjustment of inverter output power, and re-measurement of frequency change after the power system responds. The power system responds to disturbances based on the closed-loop feedback structure.

8. The method for calculating the new energy carrying capacity of a power grid according to claim 7, characterized in that: The closed-loop control loop is simulated using a dynamic simulation platform to verify the following key indicators: Frequency response: Obtain the system frequency drop and recovery process when a disturbance occurs, verify whether virtual inertia control can delay the frequency drop and the power system recovery speed; Oscillation decay: Analyzes whether the power system has persistent oscillations after a disturbance and how quickly the power system oscillation decays to a steady state through virtual inertia control. Comparative evaluation: Analyze the differences in system dynamic response with and without virtual inertia control, and quantify the improvement in power system stability achieved through control improvements.

9. The method for calculating the new energy carrying capacity of a power grid according to claim 8, characterized in that: In the two-dimensional assessment model, the horizontal axis represents the maximum accessible energy capacity percentage or the steady-state safety margin value, and the vertical axis represents the regulation index or dynamic response speed index based on frequency regulation capability and reserve capacity. Flow and dynamic simulation calculations are performed on each scenario to obtain the indicator data for the corresponding scenario and generate two-dimensional coordinate points.

10. The method for calculating the new energy carrying capacity of a power grid according to claim 9, characterized in that: Quantifying the impact of new energy access: For different renewable energy access ratios and output fluctuation scenarios, supply indicators and regulation indicators are calculated separately, and each scenario point is calibrated in the two-dimensional model; Based on the indicator data under all scenarios, the safety margin, redundancy rate and risk index of each scenario are calculated, and statistical methods are used to quantitatively describe the impact of renewable energy integration on the power system's carrying capacity; Find the boundary points based on the distribution of each scenario and determine the maximum proportion of new energy that the system can stably accept under each scenario.

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