Power dispatching method and device suitable for new energy fluctuation, terminal equipment and storage medium

Abstract

The invention discloses a power dispatching method and device suitable for new energy fluctuation, terminal equipment and a storage medium, and belongs to the technical field of power dispatching, and the method comprises the steps: constructing a target function with the minimum voltage deviation of a converter station as a target, and constructing a new energy operation model, an energy storage response model and a voltage safety domain constraint; through coupling solution of a new energy operation model, an energy storage element response model, a voltage safety domain constraint and a target function, power scheduling parameters under collaborative optimization of converter station voltage regulation, new energy output and energy storage charging and discharging can be obtained. The voltage deviation is controlled within a safe range, the adaptive capacity of the power grid to the uncertainty of the new energy is improved, the problems that the anti-fluctuation capacity is weak and the voltage stability is difficult to guarantee due to independent scheduling of the three in the prior art are solved, and safe and stable operation and efficient scheduling of the power grid under the fluctuation of the new energy are achieved.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a power dispatching method, apparatus, terminal equipment, and storage medium suitable for power dispatching under new energy fluctuations. Background Technology

[0002] In power systems, new energy sources (such as wind and solar power) have the advantages of abundant resources and environmental friendliness, but their output is affected by meteorological conditions and is therefore uncertain. This uncertainty in new energy output has multifaceted impacts on power system operation, threatening not only the safe and stable operation of the power system but also potentially leading to frequency and voltage instability, and increasing the difficulty of optimal power system dispatch.

[0003] In traditional power dispatching, converter station voltage regulation, renewable energy output management, and energy storage charging and discharging control typically employ independent and decentralized dispatching models. For example, they rely solely on real-time monitored voltage data to adjust converter station power output to maintain voltage stability, failing to incorporate the regulation capabilities of energy storage systems into the dispatching strategy, resulting in a simplistic voltage regulation approach. Furthermore, they lack modeling for the output fluctuations of renewable energy sources such as wind and solar power, and do not consider the impact of uncertainties in renewable energy output on voltage during actual operation, leading to a disconnect between dispatching schemes and actual needs.

[0004] Because the converter station voltage regulation, new energy output, and energy storage charging and discharging are independently scheduled without a linkage mechanism, traditional technologies cannot quickly and effectively adjust the scheduling strategy when the output of new energy suddenly fluctuates significantly. This results in weak system resistance to fluctuations and an inability to ensure the voltage stability of the power grid. Summary of the Invention

[0005] This invention provides a power dispatching method, device, terminal equipment, and storage medium suitable for power dispatching under renewable energy fluctuations. By coupling and solving the renewable energy operation model, the energy storage element response model, the voltage safety domain constraint, and the objective function, the first power dispatching parameter and the second power dispatching parameter can be obtained under the coordinated optimization of converter station voltage regulation, renewable energy output, and energy storage charging and discharging. This enables rapid response and precise suppression of renewable energy output fluctuations, and effectively solves the problem in the prior art where the converter station voltage regulation, renewable energy output, and energy storage charging and discharging do not form a coordinated dispatching mechanism, making it impossible to quickly and effectively adjust the dispatching strategy.

[0006] An embodiment of the present invention provides a power dispatching method applicable to renewable energy fluctuations, comprising:

[0007] Construct an objective function with the goal of minimizing the voltage deviation of the converter station;

[0008] Based on the operation data of new energy under several different operating scenarios, the uncertainty of new energy output is modeled and a new energy operation model is constructed.

[0009] Based on voltage data, operation data of various new energy sources, charging and discharging power data of energy storage components, and initial impedance values ​​of energy storage components, a response model for energy storage components is constructed.

[0010] Based on the DC voltage data, voltage recovery rate data, AC voltage data, and power data of the converter station in the flexible DC system, a voltage security domain constraint is constructed to ensure the voltage stability of the power grid when the output of new energy sources fluctuates.

[0011] The new energy operation model, energy storage element response model, voltage security domain constraint and objective function are solved. When the voltage deviation of the converter station is minimized, the first power scheduling parameters of the converter station and the second power scheduling parameters of the energy storage are output.

[0012] The power output of the converter station is adjusted according to the first power scheduling parameter, and the charging and discharging power of the energy storage is controlled according to the second power scheduling parameter.

[0013] Preferably, the step of solving the new energy operation model, energy storage element response model, voltage safety domain constraint, and objective function, and outputting the first power scheduling parameters of the converter station and the second power scheduling parameters of the energy storage when the voltage deviation of the converter station is minimized, includes:

[0014] Based on the aforementioned new energy operation model, energy storage element response model, and voltage security domain constraints, a mathematical model for optimizing voltage fluctuations in the power system is constructed.

[0015] Numerical integration is performed on the mathematical model to generate a nonlinear algebraic model;

[0016] The nonlinear algebraic model is solved, and when the voltage deviation of the converter station is minimized, the first power scheduling parameter and the second power scheduling parameter are output.

[0017] Preferably, the new energy operation data includes: the actual new energy output during power system operation, and the predicted new energy output based on historical new energy operation data and meteorological data; the predicted new energy output corresponds to a prediction error that follows a normal distribution.

[0018] The process involves modeling the uncertainty of new energy output based on new energy operation data under several different operating scenarios, and constructing a new energy operation model, including:

[0019] Cluster analysis is performed on the new energy operation data under different operating scenarios to output several new energy operation data groups of different operating scenario types;

[0020] Based on the actual renewable energy output, predicted renewable energy output, and prediction error in each renewable energy operation data set, a renewable energy operation model is constructed.

[0021] Preferably, the energy storage element response model includes: an energy model of the energy storage element and an impedance model for adjusting the charging and discharging characteristics of the energy storage; the new energy operation data further includes: the rate of change of the new energy output; the voltage data includes: the AC bus voltage of the energy storage access point and the maximum AC bus voltage.

[0022] The energy storage element response model is constructed based on voltage data, operation data of various new energy sources, charging and discharging power data of energy storage elements, and initial impedance values ​​of energy storage elements, including:

[0023] Based on the charging power, discharging power, and charging efficiency of the energy storage element, an energy model of the energy storage element is constructed.

[0024] An impedance model is constructed based on the initial impedance value of the energy storage element, the actual power output of the new energy source, the rate of change of the power output of the new energy source, the AC bus voltage at the energy storage access point, and the maximum AC bus voltage.

[0025] Preferably, the voltage safety domain constraint includes: DC voltage constraint, voltage recovery rate constraint, and AC voltage constraint;

[0026] The DC voltage constraint includes:

[0027]

[0028] Among them, V dc (t) represents the DC voltage at time t of the converter station. This is the lower limit of DC voltage. This is the upper limit of the DC voltage.

[0029] The voltage recovery rate constraint includes:

[0030]

[0031] The preset time step is ω, where ω is the frequency of the flexible DC system.

[0032] The AC voltage constraint includes:

[0033]

[0034] Among them, V ac (t) represents the AC voltage at time t in the converter station. The duration during which the AC voltage at the converter station at time t is less than 0.75 This represents the lower limit of the AC voltage at time t in the converter station. This represents the upper limit of the AC voltage at time t in the converter station.

[0035] Preferably, the energy model of the energy storage element includes:

[0036]

[0037] Where s(t) is the energy of the energy storage element at time t, s(t-1) is the energy of the energy storage element at time t-1, η is the charging efficiency, and P c (t) represents the charging power of the energy storage element at time t, P d (t) represents the discharge power of the energy storage element at time t;

[0038] The impedance model includes:

[0039]

[0040] Where Z(t) is the impedance of the energy storage element at time t, Z0 is the initial impedance of the energy storage element, A is the rate of change of the new energy output, and P renew (t) represents the actual new energy output at time t, V max V(t) represents the maximum AC bus voltage, and V(t) represents the AC bus voltage at the energy storage access point.

[0041] Preferably, the voltage safety domain constraint further includes: converter station reactive power constraint;

[0042] The reactive power constraint of the converter station includes:

[0043]

[0044] Among them, Q c Q(t) represents the reactive power of the converter station at time t. load (t) represents the total reactive power load of the power grid at time t, Q loss (t) represents the total reactive power loss of the power grid at time t. This is the sum of the total reactive power compensation of the power grid at time t and the reactive power of the generator at time t.

[0045] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0046] One embodiment of the present invention provides a power dispatching device suitable for new energy fluctuations, comprising: an objective function construction module, a model construction module, a dispatching parameter solving module, and a power control module;

[0047] The objective function construction module is used to construct an objective function with the goal of minimizing the voltage deviation of the converter station.

[0048] The model building module is used to model the uncertainty of new energy output based on new energy operation data under several different operating scenarios, and build a new energy operation model.

[0049] Based on voltage data, operation data of various new energy sources, charging and discharging power data of energy storage components, and initial impedance values ​​of energy storage components, a response model for energy storage components is constructed.

[0050] Based on the DC voltage data, voltage recovery rate data, AC voltage data, and power data of the converter station in the flexible DC system, a voltage security domain constraint is constructed to ensure the voltage stability of the power grid when the output of new energy sources fluctuates.

[0051] The scheduling parameter solving module is used to solve the new energy operation model, energy storage element response model, voltage safety domain constraint and objective function. When the voltage deviation of the converter station is minimal, it outputs the first power scheduling parameter of the converter station and the second power scheduling parameter of the energy storage.

[0052] The power control module is used to adjust the power output of the converter station according to the first power scheduling parameter, and to control the charging and discharging power of the energy storage according to the second power scheduling parameter.

[0053] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0054] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power dispatching method applicable to new energy fluctuations described in the above-described embodiments of the invention.

[0055] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments.

[0056] Another embodiment of the present invention provides a storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power dispatching method applicable to new energy fluctuations described in the above-described embodiments of the invention.

[0057] The following benefits can be obtained by implementing the present invention:

[0058] This invention provides a power dispatching method, device, terminal equipment, and storage medium applicable to renewable energy fluctuations. First, it constructs an objective function with the goal of minimizing voltage deviation at converter stations. It also models the uncertainty of renewable energy output using multi-scenario data to construct a renewable energy operation model. When constructing the energy storage response model, real-time data such as voltage, renewable energy output, and charging / discharging power are incorporated to calculate optimal charging / discharging parameters. A voltage safety domain constraint is constructed, incorporating the voltage stability of the power grid during renewable energy output fluctuations into the model solution to ensure that dispatching parameters remain within the safety domain. Through the renewable energy operation model, By coupling the energy storage element response model, voltage safety domain constraints, and objective function, the first and second power dispatch parameters under the coordinated optimization of converter station voltage regulation, new energy output, and energy storage charging and discharging can be obtained. This enables rapid response and precise suppression of new energy output fluctuations. Through the linkage regulation of converter station voltage regulation and energy storage charging and discharging, voltage deviations can be controlled within a safe range, improving the grid's adaptability to the uncertainty of new energy. This solves the problems of weak anti-fluctuation capability and difficulty in ensuring voltage stability caused by the independent dispatch of the three in traditional technologies, and realizes the safe and stable operation and efficient dispatch of the grid under new energy fluctuations. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating a power dispatching method applicable to new energy fluctuations, provided by an embodiment of the present invention.

[0060] Figure 2 This is a schematic diagram of the structure of a power dispatching device suitable for new energy fluctuations, provided by an embodiment of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] like Figure 1 As shown, to address the issue that due to the independent scheduling of converter station voltage regulation, renewable energy output, and energy storage charging and discharging, without a coordinated mechanism, traditional technologies cannot quickly and effectively adjust scheduling strategies when renewable energy output suddenly fluctuates significantly, resulting in weak system resilience and inability to guarantee grid voltage stability, an embodiment of this invention provides a power dispatching method suitable for renewable energy fluctuations, including:

[0063] Step S1: Construct an objective function with the goal of minimizing the voltage deviation of the converter station;

[0064] In illustrative terms, this invention uses the deviation between the actual voltage and the rated voltage of the converter station as the optimization object and establishes a mathematical objective function, such as minimizing the sum of squares of voltage deviations. Voltage stability can be taken as the core optimization objective, so that the power regulation of the converter station and the charging and discharging control of energy storage are all centered around minimizing the voltage deviation. This avoids the voltage control lag problem caused by the ambiguity of the scheduling objective in traditional technologies and ensures that the scheduling parameters prioritize grid voltage stability.

[0065] Step S2: Based on the new energy operation data under several different operating scenarios, model the uncertainty of new energy output and construct a new energy operation model;

[0066] Based on voltage data, operation data of various new energy sources, charging and discharging power data of energy storage components, and initial impedance values ​​of energy storage components, a response model for energy storage components is constructed.

[0067] Based on the DC voltage data, voltage recovery rate data, AC voltage data, and power data of the converter station in the flexible DC system, a voltage security domain constraint is constructed to ensure the voltage stability of the power grid when the output of new energy sources fluctuates.

[0068] In illustrative terms, this invention can collect power output data of new energy sources (such as wind power and photovoltaics) under different weather and season scenarios, and quantify the volatility and uncertainty of power output by modeling through probabilistic statistics, stochastic processes and other methods, such as the probability distribution and fluctuation range of power output. Then, the uncertainty of new energy power output can be incorporated into the scheduling strategy to adapt to the power output fluctuations in actual operation.

[0069] This invention can integrate voltage monitoring data, real-time output values ​​of new energy sources, energy storage charging and discharging power and impedance parameters to establish a mapping relationship between energy storage charging and discharging behavior and system voltage changes, thereby obtaining an energy storage element response model. This enables the linkage between the dynamic adjustment capability of energy storage and voltage control requirements, and realizes the real-time response of energy storage to voltage fluctuations.

[0070] This invention can also set safety boundaries for scheduling parameters by constructing voltage safety domain constraints, thereby preventing voltage safety limits from being exceeded when the converter station regulates voltage or the energy storage charges and discharges, and ensuring the safety of grid operation.

[0071] Step S3: Solve the new energy operation model, energy storage element response model, voltage security domain constraint and objective function. When the voltage deviation of the converter station is minimized, output the first power scheduling parameters of the converter station and the second power scheduling parameters of the energy storage.

[0072] In illustrative terms, this invention can combine the new energy operation model, energy storage response model, voltage safety domain constraint and objective function, and solve them through an optimization algorithm to obtain the optimal power regulation parameters (first parameters) of the converter station and the optimal charging and discharging parameters (second parameters) of the energy storage.

[0073] Therefore, this invention can achieve coordinated optimization of converter station voltage regulation, new energy power output, and energy storage charging and discharging, avoiding strategy conflicts or regulation lags caused by the independent scheduling of the three in traditional technologies.

[0074] By synchronously determining the scheduling parameters of the converter station and energy storage, the two can form a linkage adjustment when the output of new energy sources fluctuates. For example, when the converter station adjusts the voltage, the energy storage can charge and discharge to replenish energy, which can quickly suppress voltage deviation and improve the system's ability to resist fluctuations.

[0075] Step S4: Adjust the power output of the converter station according to the first power scheduling parameter, and control the charging and discharging power of the energy storage according to the second power scheduling parameter.

[0076] Indicatively, this invention can adjust the active / reactive power output of the converter station according to the first parameter obtained by solving, and control the charging and discharging power of the energy storage according to the second parameter, so as to realize the real-time execution of the scheduling strategy.

[0077] This invention can mitigate the impact of new energy output fluctuations on voltage in real time through the coordinated operation of converter stations and energy storage, keeping voltage deviations within a safe range and ensuring stable grid operation.

[0078] Specifically, when fluctuations in renewable energy output cause voltage deviations from the target value, the system not only adjusts the power output through the converter station but also utilizes energy storage for charging and discharging based on the energy storage response model. The combined effect of these two mechanisms allows the voltage to quickly recover to a safe range. For example, when renewable energy output drops sharply, the converter station increases its active power output to stabilize the voltage, while energy storage discharges to assist in voltage regulation, avoiding the delays or poor performance caused by insufficient capacity in traditional single-method voltage regulation.

[0079] For step S1, in a preferred embodiment, to ensure voltage safety and stability, the objective function selected by the present invention is to minimize the integral of the square of the deviation of the DC voltage of the converter station, which is:

[0080]

[0081] Among them, V dc (t) represents the DC voltage at time t of the converter station, V. ref (t) represents the DC voltage reference value of the converter station at time t. f Let t be the integration time, and t0 be the preset initial time.

[0082] When performing multi-model coupled solutions, adjusting the power output of the converter station and the charging and discharging power of the energy storage will cause a balance between the active and reactive power of the power grid, thereby affecting V. dc The value of (t) will further affect the integral of the square of the deviation of the DC voltage of the converter station.

[0083] It is understandable that in the process of multi-model coupling solution, the adjustment of converter station power output and energy storage charging and discharging power will directly affect the balance of active and reactive power of the power grid. When the power output of new energy sources fluctuates and causes power imbalance, the active power deficit can be compensated by adjusting the active power of the converter station, and the reactive power can be adjusted to support voltage stability; while energy storage charging and discharging can smooth out power fluctuations by dynamically absorbing and discharging energy.

[0084] The aforementioned changes in power balance are reflected in the dynamic changes of the DC voltage at the converter station in real time. For example, active power imbalance can cause low-frequency fluctuations in DC voltage, while insufficient reactive power can lead to voltage amplitude deviations. The integral of the square of the DC voltage deviation optimized by the objective function is essentially a constraint on the entire voltage dynamic process. By minimizing this integral value, the power dispatch scheme that minimizes the cumulative voltage deviation over time is prioritized, ensuring coordinated regulation between the converter station and energy storage. This allows for both rapid suppression of voltage fluctuations and long-term stable control. In this process, power balance and voltage deviation are bidirectionally coupled through electromagnetic transient characteristics, ensuring that the optimization result of the objective function meets the dynamic stability requirements of the power grid.

[0085] For step S2, in a preferred embodiment, firstly, a new energy operation model is constructed:

[0086] The new energy operation data includes: the actual new energy output during power system operation, and the predicted new energy output based on historical new energy operation data and meteorological data; the predicted new energy output corresponds to a prediction error that follows a normal distribution.

[0087] Therefore, based on the new energy operation data under several different operating scenarios, the uncertainty of new energy output is modeled to construct a new energy operation model, including:

[0088] Cluster analysis is performed on the new energy operation data under different operating scenarios to output several new energy operation data groups of different operating scenario types;

[0089] Based on the actual renewable energy output, predicted renewable energy output, and prediction error in each renewable energy operation data set, a renewable energy operation model is constructed.

[0090] In a schematic way, by clustering analysis of renewable energy operation data (including actual output, predicted output, and prediction error) under different operating scenarios, the uncertainty of renewable energy output can be classified and summarized according to scenario type (such as high wind speed and high sunshine, low wind speed and cloudy). Since the prediction error follows a normal distribution, the clustered data groups can more accurately reflect the probability distribution characteristics of renewable energy output under each scenario, avoiding the simplification of complex uncertainties by traditional single models, and providing more realistic input conditions for subsequent scheduling strategies.

[0091] Clustering generates different scenario-type renewable energy operation data sets, which can cover the fluctuation patterns of renewable energy output under different meteorological conditions and seasonal characteristics. For example, the data can be divided into "strong fluctuation scenarios" and "stable scenarios," enabling the model to call the corresponding output characteristic parameters for different scenario types, avoiding prediction bias caused by ignoring scenario differences in traditional modeling.

[0092] The constructed renewable energy operation model combines actual output with prediction errors, enabling the quantification of the potential impact of renewable energy output on grid voltage under different scenarios before scheduling. This allows for proactive prediction of renewable energy fluctuation trends during converter station voltage regulation and energy storage charging / discharging scheduling, rather than passive adjustments based solely on real-time data. For example, in high-fluctuation scenarios, the model can prompt energy storage to reserve more regulation capacity, working in conjunction with converter stations to cope with upcoming output fluctuations and improving the system's resilience to fluctuations from the source.

[0093] Traditional technologies lack modeling for fluctuations in renewable energy output, leading to the frequent failure of scheduling schemes in actual operation. This invention, however, constructs a renewable energy operation model through cluster analysis, transforming uncertainty into quantifiable probability parameters (such as output fluctuation ranges under different scenarios). These parameters are incorporated as constraints during scheduling optimization, ensuring that the solved power scheduling parameters (converter station power, energy storage charging and discharging power) remain effective under various possible renewable energy fluctuation scenarios. This avoids scheduling strategy failures due to assumptions about a single scenario, fundamentally solving the problem of scheduling schemes being out of sync with actual needs in traditional technologies.

[0094] Specifically, the new energy operation model is as follows:

[0095] P renew (t)=P pred (t)·(1+ε(t));

[0096] Among them, P pred P(t) represents the predicted output of new energy sources, ε(t) represents the prediction error, both following a normal distribution. renew (t) represents the actual output of new energy sources.

[0097] Since the prediction error of new energy power output follows a normal distribution, this invention can use Monte Carlo sampling to extract a massive number of deterministic operating scenarios to describe new energy power output. This invention employs RC-means clustering to cluster these massive operating scenarios. Illustratively, RC-means clustering is an improved clustering algorithm. Its core idea originates from traditional K-means clustering, but it has been optimized in data processing and clustering logic. "RC" can generally be understood as "Robust Clustering" or "Refined Clustering," aiming to solve the problems of traditional K-means algorithm's sensitivity to noisy data and the susceptibility of initial cluster centers to randomness, thereby improving the stability and accuracy of clustering results.

[0098] The specific process of RC-means clustering is as follows:

[0099] Calculate the clustering coefficient for each running scenario, and select the N smallest clusters as the cluster centers:

[0100]

[0101] Where δ is the clustering coefficient for each scene, Cov is the covariance, Var is the variance, m is the number of scenes, and ω is the number of clustering coefficients. i The covariance coefficient is generally taken as [value missing]. P predi and P renewi Let R be the predicted power and the actual power for the i-th scene, respectively. R is a dynamic natural factor, calculated using the following formula:

[0102]

[0103] Calculate the pseudocosine similarity from each scene to the cluster center, and group the corresponding scenes into the cluster with the highest similarity. The pseudocosine similarity is calculated as follows:

[0104]

[0105] Where cosα is the pseudocosine similarity, P renew and P 聚心 These are vectors composed of active power in a certain scenario and vectors composed of active power in a certain focal scenario, respectively.

[0106] Update cluster centers: Calculate the mean of each cluster scenario as the new cluster center.

[0107]

[0108] Among them, P 新聚心 Let k be the new cluster center vector, and k be the number of scenes in a certain cluster.

[0109] Using the new clustering vector, the quasi-cosine similarity from each scene to the clustering center is recalculated, and the scenes are re-clustered. This process is iterated until the maximum number of iterations is reached.

[0110] After clustering, this invention can obtain N types of scene clusters and N cluster centers. The N types of scene clusters can be represented by their respective N cluster centers. Thus, this invention can reduce a massive number of running scenarios to N key scenarios, reducing the pressure of computation and analysis.

[0111] Then, construct the energy storage element response model:

[0112] The energy storage element response model includes: an energy model of the energy storage element and an impedance model for adjusting the charging and discharging characteristics of the energy storage; the new energy operation data also includes: the rate of change of the new energy output; the voltage data includes: the AC bus voltage at the energy storage access point and the maximum AC bus voltage.

[0113] Therefore, based on voltage data, operational data of various new energy sources, charging and discharging power data of energy storage elements, and initial impedance values ​​of energy storage elements, a response model for the energy storage element is constructed, including:

[0114] Based on the charging power, discharging power, and charging efficiency of the energy storage element, an energy model of the energy storage element is constructed.

[0115] An impedance model is constructed based on the initial impedance value of the energy storage element, the actual power output of the new energy source, the rate of change of the power output of the new energy source, the AC bus voltage at the energy storage access point, and the maximum AC bus voltage.

[0116] Specifically, energy storage components such as supercapacitors and lithium batteries can provide second-level power compensation to cope with voltage fluctuations caused by fluctuations in new energy output. The energy models for these energy storage components include:

[0117]

[0118] Where s(t) is the energy of the energy storage element at time t, s(t-1) is the energy of the energy storage element at time t-1, η is the charging efficiency, and P c (t) represents the charging power of the energy storage element at time t, P d (t) represents the discharge power of the energy storage element at time t;

[0119] Furthermore, to address fluctuations in new energy output, this invention can also design a dynamic virtual impedance, i.e., an impedance model, for energy storage components, including:

[0120]

[0121] Where Z(t) is the impedance of the energy storage element at time t, which can also be understood as the virtual impedance; Z0 is the initial impedance value of the energy storage element; A is the rate of change of the new energy output; and P... renew (t) represents the actual new energy output at time t, V max V(t) represents the maximum AC bus voltage, and V(t) represents the AC bus voltage at the energy storage access point.

[0122] In a schematic way, the energy model can accumulate charging and discharging power and efficiency parameters to calculate the energy of the energy storage at any time. It can accurately reflect the energy throughput process of energy storage components such as supercapacitors and lithium batteries with a second-level response. For example, when the output of new energy sources increases sharply, the model can quickly calculate the energy storage charging amount to avoid overcharging; when the output drops sharply, it can simultaneously calculate whether the discharge capacity meets the power compensation requirements, solving the problem of blind scheduling caused by the black box of energy storage status in traditional technologies.

[0123] Furthermore, in the impedance model, the rate of change of new energy output and the voltage deviation V are introduced. max -V(t) enables the energy storage impedance to be adjusted in real time according to the grid conditions. For example, when the output of new energy sources drops sharply, causing a voltage drop, the model calculates a negative rate of change in output, reduces the driving impedance, and the energy storage discharges quickly in a low-impedance mode, improving the current output capability, compensating for the power gap, and suppressing the voltage drop.

[0124] Moreover, the virtual impedance and voltage deviation V max -V(t) is inversely proportional, so the impedance can automatically increase when the voltage is close to the safe upper limit, limiting the energy storage charging power and avoiding voltage overshoot; when the voltage drops sharply, the impedance decreases and releases the maximum discharge capacity, so that the energy storage can automatically match the optimal impedance parameters under different fluctuation scenarios (such as sudden reduction of photovoltaic power and sudden increase of wind power), solving the problem that traditional fixed impedance cannot adapt to complex working conditions.

[0125] Finally, voltage safety domain constraints are constructed:

[0126] The voltage safety domain constraints include: DC voltage constraints, voltage recovery rate constraints, AC voltage constraints, and converter station reactive power constraints.

[0127] Therefore, illustratively speaking, when constructing the aforementioned constraints based on the converter station DC voltage data, converter station voltage recovery rate data, converter station AC voltage data, and converter station power data of the flexible DC system, we have:

[0128] The DC voltage constraint includes:

[0129]

[0130] Among them, V dc (t) represents the DC voltage at time t of the converter station. This is the lower limit of DC voltage. This is the upper limit of the DC voltage.

[0131] The voltage recovery rate constraint includes:

[0132]

[0133] Among them, V dc ′(t) represents the voltage recovery rate at time t, V dc (t0) is the DC voltage at the initial time t0, λ is the preset time step, and ω is the frequency of the flexible DC system;

[0134] The AC voltage constraint includes:

[0135]

[0136] Among them, V ac (t) represents the AC voltage at time t in the converter station. The duration during which the AC voltage at the converter station at time t is less than 0.75 This represents the lower limit of the AC voltage at time t in the converter station. This represents the upper limit of the AC voltage at time t in the converter station.

[0137] The reactive power constraint of the converter station includes:

[0138]

[0139] Among them, Q c Q(t) represents the reactive power of the converter station at time t. load (t) represents the total reactive power load of the power grid at time t, Q loss (t) represents the total reactive power loss of the power grid at time t. This is the sum of the total reactive power compensation of the power grid at time t and the reactive power of the generator at time t.

[0140] To illustrate, if the duration of the AC voltage at converter station at time t being less than 0.75 is not greater than 1, then voltage safety during transient faults in the power grid can be ensured.

[0141] AC voltage constraints and converter station reactive power constraints are actually boundary equations of the steady-state voltage security domain of the AC grid side, which can ensure that the AC side has sufficient reactive power support in steady state and operates in a reasonable state.

[0142] The above four types of constraints construct safety boundaries from four dimensions: DC voltage, voltage change rate, AC voltage, and reactive power, forming a three-dimensional protection for grid voltage stability. This covers the main voltage problems that may be caused by fluctuations in new energy sources, such as overvoltage, undervoltage, sudden changes, and reactive power imbalance. When the AC voltage drops, the DC voltage constraint and the reactive power constraint will simultaneously trigger converter station power adjustment and energy storage reactive power compensation.

[0143] For step S3, in a preferred embodiment, solving the new energy operation model, energy storage element response model, voltage safety domain constraint, and objective function, and outputting the first power scheduling parameters of the converter station and the second power scheduling parameters of the energy storage when the voltage deviation of the converter station is minimized, includes:

[0144] Based on the aforementioned new energy operation model, energy storage element response model, and voltage security domain constraints, a mathematical model for optimizing voltage fluctuations in the power system is constructed.

[0145] Numerical integration is performed on the mathematical model to generate a nonlinear algebraic model;

[0146] The nonlinear algebraic model is solved, and when the voltage deviation of the converter station is minimized, the first power scheduling parameter and the second power scheduling parameter are output.

[0147] In a preferred embodiment, the mathematical model can be expressed as:

[0148] P renew (t)=P pred (t)·(1+ε(t));

[0149]

[0150] To illustrate, the mathematical model described above is an optimization model in the continuous time domain. When solving it, it needs to be discretized into a system of nonlinear algebraic equations through numerical integration, i.e., a nonlinear algebraic model.

[0151] Solving this nonlinear algebraic model actually involves simultaneously solving for both the nonlinear algebraic model and the objective function. The core of the objective function is the DC voltage V of the converter station. dc (t) deviation from the rated value, V dc The change in (t) is actually determined by the system power balance and circuit characteristics, which is the implicit connection point between the constraints and the objective function.

[0152] Specifically, in a flexible DC system, the DC voltage V of the converter station... dc (t) and the reactive power Q flowing through the converter station c (t), line impedance, etc. have a clear physical relationship.

[0153] Adjusting the power output of converter stations and the charging and discharging power of energy storage will affect the balance of active and reactive power in the power grid, thereby influencing V. dc The value of (t) will further affect the integral of the square of the deviation of the DC voltage of the converter station.

[0154] In addition, fluctuations in new energy sources and the charging and discharging behavior of energy storage will directly affect Q. c (t), and Q c (t) also affects V through the physical relationship between voltage and power. dc (t), ultimately related to the objective function (V) dc The deviation of (t) forms a closed-loop correlation.

[0155] In a preferred embodiment, solving the new energy operation model, energy storage element response model, voltage safety domain constraints, and objective function, and outputting the first power scheduling parameters of the converter station and the second power scheduling parameters of the energy storage when the voltage deviation of the converter station is minimized, includes:

[0156] Based on the new energy operation model, energy storage element response model, and voltage security domain constraints, differential equations for describing the operation and change characteristics of the power system, algebraic equations for describing the power balance relationship of the power system, and inequality constraints for characterizing the voltage security domain boundary are constructed.

[0157] Based on the aforementioned differential equations, algebraic equations, inequality constraints, and objective function, a mathematical model for optimizing voltage fluctuations in power systems is constructed.

[0158] Numerical integration is performed on the mathematical model to generate a nonlinear algebraic model;

[0159] The nonlinear algebraic model is solved, and when the voltage deviation of the converter station is minimized, the first power scheduling parameter and the second power scheduling parameter are output.

[0160] Furthermore, the differential equations, algebraic equations, and inequality constraints all include: state variables, algebraic variables, and control variables; wherein, the state variables include: the energy of the energy storage element, the DC voltage of the converter station, the voltage recovery rate of the converter station, and the AC voltage of the converter station; the algebraic variables include: the output of new energy sources, the power of each node in the power grid, the charging and discharging power of energy storage, the total reactive power loss of the power grid, the total reactive power compensation of the power grid, and the reactive power of the generator; the control variables include: the power of the converter station and the charging and discharging power of the energy storage element;

[0161] The specific process for transforming into a nonlinear algebraic model is as follows:

[0162] Based on Hermitian polynomials, the interpolation sets of state variables in the mathematical model are linearly superimposed to generate numerical integral coefficients; wherein, the numerical integral coefficients are used to quantify the impact of fluctuations in state variables under the uncertainty of new energy output on the integration results.

[0163] The time domain corresponding to the mathematical model is divided into several sub-intervals according to a preset time interval;

[0164] For each subinterval, using the initial state variables as the recursive basis, and combining the initial control variables, numerical integral coefficients, the total number of subintervals, and the basic parameters of the subintervals, the interpolation set of state variables is combined using the tensor product method to predict the predicted state variables corresponding to each subinterval.

[0165] Wherein, the initial state variable and the initial control variable are the state variable and control variable corresponding to the initial moment in the time set, respectively; the basic parameters of the sub-interval include: the length of the sub-interval, the interval weight used to reflect the contribution of the sub-interval to the integral, and the number of interpolation points in the sub-interval; the interpolation set of the state variable is: the set of values ​​of the state variable under different interpolation levels;

[0166] By combining the algebraic equations and the predicted state variables corresponding to each subinterval, the inequality constraints are transformed into algebraic inequalities, thus constructing a nonlinear algebraic model.

[0167] As an illustration, the random fluctuations in the output of new energy sources (such as the intermittency of wind power and photovoltaics) and the nonlinear characteristics of the power system itself (such as the switching action of converter stations and the changes in the charging and discharging efficiency of energy storage) lead to strong nonlinearity in the mathematical model. This invention transforms the nonlinear dynamic process into a computable algebraic relationship through Hermitian polynomials and numerical integration methods.

[0168] Since the mathematical model is based on differential equations, algebraic equations, inequality constraints, and objective functions, the differential equations and inequality constraints cannot be solved directly. After being transformed into a nonlinear algebraic model, mature optimization algorithms, such as Newton's method and sequential quadratic programming, can be used to solve it numerically, thereby quickly obtaining the optimal power scheduling parameters.

[0169] In a preferred embodiment, when constructing a mathematical model based on differential equations, algebraic equations, inequality constraints, and an objective function, mathematical modeling is actually performed on the AC and DC components of the power grid, resulting in the following mathematical model:

[0170]

[0171] 0 = g(x(t), y(t), u(t));

[0172] 0≤h(x(t),y(t),u(t));

[0173] Where x(t) is the column vector of state variables, y(t) is the column vector of algebraic variables, and u(t) is the column vector of control variables;

[0174] It is understandable that the above mathematical model is used to describe the dynamic characteristics, steady-state constraints and operating boundaries of the power system (including AC and DC components), and it corresponds to the core mathematical expression of power system dynamic optimization and voltage safety control.

[0175] x(t) is the time derivative of the state variable, f(·) represents the differential equation, g(·) represents the algebraic equation, h(·) defines the boundary conditions of the inequality constraint, and 0≤h(x(t),y(t),u(t)) represents the inequality constraint.

[0176] As mentioned above, the mathematical model contains differential equations, which require numerical integration for solution. This invention can solve the time interval [t0, t...] f The data is divided into multiple equal-length subintervals, and fast numerical integration is performed within each subinterval. The integration method is as follows:

[0177] For the P-th subinterval, the predicted value of its state variable is based on the initial state variable of the first interval and predicted using the tensor product method. Then, using the initial state variable as the recursive basis, combined with the initial control variable, numerical integration coefficients, the total number of subintervals, and the basic parameters of the subintervals, the predicted state variable for each subinterval is calculated using the tensor product method by combining the interpolation set of state variables. The predicted state variable for each subinterval can be calculated using the following formula:

[0178]

[0179] Where x(t0) is the state variable corresponding to the initial time t0 in the time set, i.e., the initial state variable, and u(t0) is the control variable corresponding to the initial time t0 in the time set, i.e., the initial control variable. Let be the predicted state variable for subinterval p, φ be the numerical integration coefficient, h be the length of the subinterval, K be the weight of the subinterval, z be the number of subintervals, q be the number of interpolation points inserted in each subinterval, and i be the number of interpolation points inserted in each subinterval. n For the interpolation level of the nth state variable, Let n be the set of interpolations for the nth state variable.

[0180] Since the predicted output of new energy sources follows a Gaussian distribution, the numerical integration coefficient φ can be obtained by linear superposition of Hermitian polynomials, resulting in:

[0181]

[0182] Where H(r) is a Hermitian polynomial.

[0183] After numerical integration, the differential equation has been transformed into the predicted state variables corresponding to each subinterval. Based on the predicted state variables corresponding to each subinterval and the derived algebraic equations, a nonlinear algebraic model containing algebraic inequalities can be formed. By calling the commercial solver BARON, the optimal control strategy, namely the first power scheduling parameter and the second power scheduling parameter, can be obtained.

[0184] Therefore, the embodiments of the present invention can transform the mathematical model of a power system containing differential equations into a nonlinear algebraic model. Essentially, it transforms a continuous dynamic problem that cannot be directly solved into a discrete algebraic problem that can be efficiently calculated. It can adapt to the strong nonlinearity and uncertainty of the power system's dispatch problem and can cope with the complex voltage control requirements under the high proportion of new energy access.

[0185] In a preferred embodiment, the mathematical model can be expressed as:

[0186] min J;

[0187]

[0188] 0 = g(x(t), y(t), u(t));

[0189] 0≤h(x(t),y(t),u(t));

[0190] Where J is the objective function, x(t) is the column vector of state variables, y(t) is the column vector of algebraic variables, and u(t) is the column vector of control variables; the constraints are, in order, differential equations, algebraic equations, and inequality equations.

[0191] In this invention, the objective function is the integral of the voltage fluctuation deviation over time: This invention can define time intervals The system is divided into multiple equal-length subintervals, and fast numerical integration is performed within each subinterval. The integration method is as follows:

[0192] For the P-th subinterval, the predicted value of its state variable is based on the initial state variable of the first interval and predicted using the tensor product method. Then, using the initial state variable as the recursive basis, combined with the initial control variable, numerical integration coefficients, the total number of subintervals, and the basic parameters of the subintervals, the predicted state variable for each subinterval is calculated using the tensor product method by combining the interpolation set of state variables. The predicted state variable for each subinterval can be calculated using the following formula:

[0193]

[0194] Where x(t0) is the state variable corresponding to the initial time t0 in the time set, i.e., the initial state variable, and u(t0) is the control variable corresponding to the initial time t0 in the time set, i.e., the initial control variable. Let be the predicted state variable for subinterval p, φ be the numerical integration coefficient, h be the length of the subinterval, K be the weight of the subinterval, z be the number of subintervals, q be the number of interpolation points inserted in each subinterval, and i be the number of interpolation points inserted in each subinterval. n For the interpolation level of the nth state variable, Let n be the set of interpolations for the nth state variable.

[0195] Since the predicted output of new energy sources follows a Gaussian distribution, the numerical integration coefficient φ can be obtained by linear superposition of Hermitian polynomials, resulting in:

[0196]

[0197] Where H(r) is a Hermitian polynomial.

[0198] For algebraic equations and inequality equations, because in the time interval The state does not need to remain continuous and can change abruptly. Therefore, to accommodate the nonlinearization of the state variables, the algebraic equations and inequality equations are respectively transformed into:

[0199]

[0200] in, It is a Lagrange polynomial. t p-1 It refers to discrete historical points in time, within a time interval. One of a series of pre-defined discrete moments is used as the node time for Lagrange polynomial interpolation.

[0201] Therefore, the mathematical model used to optimize voltage fluctuations in a power system can be transformed into the following nonlinear optimization model:

[0202] min J;

[0203]

[0204] Therefore, this invention transforms a continuous dynamic problem into a discrete algebraic problem, which can be adapted to the highly nonlinear and uncertain scheduling problems of power systems.

[0205] For step S4, in a preferred embodiment, when the output of new energy fluctuates rapidly, the first power dispatching parameter of the converter station and the second power dispatching parameter of the energy storage are solved by the present invention; when it is found that the converter station needs to increase the active power output by 20MW and the reactive power support voltage by 5Mvar, the active power output of the converter station can be increased, and synchronous compensation can be performed by increasing the reactive power to suppress the voltage drop trend.

[0206] When the solution shows that the energy storage needs to discharge at a power of 10MW (lasting for 30 minutes, releasing 5MWh of energy), the energy storage is controlled to start the discharge mode, reaching a power output of 10MW within 100ms, in conjunction with the converter station to fill the remaining 10MW active power deficit.

[0207] To illustrate, when the output of new energy sources fluctuates rapidly, energy storage components can adjust their charging and discharging power to smooth out voltage fluctuations. For example, when the output of new energy sources suddenly increases, the energy storage charges to absorb the excess power and avoid voltage overshoot; when the output of new energy sources suddenly decreases, the energy storage discharges to make up for the power gap and suppress voltage drop.

[0208] like Figure 2 As shown, based on the above embodiments of various power dispatching methods applicable to new energy fluctuations, the present invention provides corresponding device embodiments;

[0209] One embodiment of the present invention provides a power dispatching device suitable for new energy fluctuations, comprising: an objective function construction module, a model construction module, a dispatching parameter solving module, and a power control module;

[0210] The objective function construction module is used to construct an objective function with the goal of minimizing the voltage deviation of the converter station.

[0211] The model building module is used to model the uncertainty of new energy output based on new energy operation data under several different operating scenarios, and build a new energy operation model.

[0212] Based on voltage data, operation data of various new energy sources, charging and discharging power data of energy storage components, and initial impedance values ​​of energy storage components, a response model for energy storage components is constructed.

[0213] Based on the DC voltage data, voltage recovery rate data, AC voltage data, and power data of the converter station in the flexible DC system, a voltage security domain constraint is constructed to ensure the voltage stability of the power grid when the output of new energy sources fluctuates.

[0214] The scheduling parameter solving module is used to solve the new energy operation model, energy storage element response model, voltage safety domain constraint and objective function. When the voltage deviation of the converter station is minimal, it outputs the first power scheduling parameter of the converter station and the second power scheduling parameter of the energy storage.

[0215] The power control module is used to adjust the power output of the converter station according to the first power scheduling parameter, and to control the charging and discharging power of the energy storage according to the second power scheduling parameter.

[0216] It should be noted that the device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0217] Those skilled in the art will understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0218] Based on the above embodiments of various power dispatching methods applicable to new energy fluctuations, the present invention provides corresponding embodiments of terminal equipment.

[0219] One embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a power dispatching method applicable to new energy fluctuations as described in any embodiment of the present invention.

[0220] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0221] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0222] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device or other volatile solid-state storage device.

[0223] Based on the above embodiments of various power dispatching methods applicable to new energy fluctuations, the present invention provides corresponding embodiments of storage media.

[0224] One embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a power dispatching method applicable to new energy fluctuations as described in any embodiment of the present invention.

[0225] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0226] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power dispatching method applicable to new energy fluctuations, characterized in that, include: Construct an objective function with the goal of minimizing the voltage deviation of the converter station; Based on the operation data of new energy under several different operating scenarios, the uncertainty of new energy output is modeled and a new energy operation model is constructed. Based on voltage data, operation data of various new energy sources, charging and discharging power data of energy storage components, and initial impedance values ​​of energy storage components, a response model for energy storage components is constructed. Based on the DC voltage data, voltage recovery rate data, AC voltage data, and power data of the converter station in the flexible DC system, a voltage security domain constraint is constructed to ensure the voltage stability of the power grid when the output of new energy sources fluctuates. The new energy operation model, energy storage element response model, voltage security domain constraint and objective function are solved. When the voltage deviation of the converter station is minimized, the first power scheduling parameters of the converter station and the second power scheduling parameters of the energy storage are output. The power output of the converter station is adjusted according to the first power scheduling parameter, and the charging and discharging power of the energy storage is controlled according to the second power scheduling parameter.

2. The power dispatching method applicable to new energy fluctuations as described in claim 1, characterized in that, The process involves solving the new energy operation model, energy storage component response model, voltage safety domain constraints, and objective function. When the voltage deviation at the converter station is minimized, the first power scheduling parameters for the converter station and the second power scheduling parameters for the energy storage are output, including: Based on the aforementioned new energy operation model, energy storage element response model, and voltage security domain constraints, a mathematical model for optimizing voltage fluctuations in the power system is constructed. Numerical integration is performed on the mathematical model to generate a nonlinear algebraic model; The nonlinear algebraic model is solved, and when the voltage deviation of the converter station is minimized, the first power scheduling parameter and the second power scheduling parameter are output.

3. The power dispatching method applicable to new energy fluctuations as described in claim 2, characterized in that, The new energy operation data includes: the actual new energy output during power system operation, and the predicted new energy output based on historical new energy operation data and meteorological data; the predicted new energy output corresponds to a prediction error that follows a normal distribution. The process involves modeling the uncertainty of new energy output based on new energy operation data under several different operating scenarios, and constructing a new energy operation model, including: Cluster analysis is performed on the new energy operation data under different operating scenarios to output several new energy operation data groups of different operating scenario types; Based on the actual renewable energy output, predicted renewable energy output, and prediction error in each renewable energy operation data set, a renewable energy operation model is constructed.

4. A power dispatching method applicable to new energy fluctuations as described in claim 3, characterized in that, The energy storage element response model includes: an energy model of the energy storage element and an impedance model for adjusting the charging and discharging characteristics of the energy storage; the new energy operation data also includes: the rate of change of the new energy output; the voltage data includes: the AC bus voltage at the energy storage access point and the maximum AC bus voltage. The energy storage element response model is constructed based on voltage data, operation data of various new energy sources, charging and discharging power data of energy storage elements, and initial impedance values ​​of energy storage elements, including: Based on the charging power, discharging power, and charging efficiency of the energy storage element, an energy model of the energy storage element is constructed. An impedance model is constructed based on the initial impedance value of the energy storage element, the actual power output of the new energy source, the rate of change of the power output of the new energy source, the AC bus voltage at the energy storage access point, and the maximum AC bus voltage.

5. A power dispatching method applicable to new energy fluctuations as described in claim 4, characterized in that, The voltage safety domain constraints include: DC voltage constraints, voltage recovery rate constraints, and AC voltage constraints; The DC voltage constraint includes: Among them, V dc (t) represents the DC voltage at time t of the converter station. This is the lower limit of DC voltage. This is the upper limit of the DC voltage. The voltage recovery rate constraint includes: Among them, V dc ′(t) represents the voltage recovery rate at time t, V dc (t0) is the DC voltage at the initial time t0, λ is the preset time step, and ω is the frequency of the flexible DC system; The AC voltage constraint includes: Among them, V ac (t) represents the AC voltage at time t in the converter station. The duration during which the AC voltage at the converter station at time t is less than 0.75 This represents the lower limit of the AC voltage at time t in the converter station. This represents the upper limit of the AC voltage at time t in the converter station.

6. A power dispatching method applicable to new energy fluctuations as described in claim 5, characterized in that, The energy model of the energy storage element includes: Where s(t) is the energy of the energy storage element at time t, s(t-1) is the energy of the energy storage element at time t-1, η is the charging efficiency, and P c (t) represents the charging power of the energy storage element at time t, P d (t) represents the discharge power of the energy storage element at time t; The impedance model includes: Where Z(t) is the impedance of the energy storage element at time t, Z0 is the initial impedance of the energy storage element, A is the rate of change of the new energy output, and P renew (t) represents the actual new energy output at time t, V max V(t) represents the maximum AC bus voltage, and V(t) represents the AC bus voltage at the energy storage access point.

7. A power dispatching method applicable to new energy fluctuations as described in claim 6, characterized in that, The voltage safety domain constraint also includes: reactive power constraint of the converter station; The reactive power constraint of the converter station includes: Among them, Q c Q(t) represents the reactive power of the converter station at time t. load (t) represents the total reactive power load of the power grid at time t, Q loss (t) represents the total reactive power loss of the power grid at time t. This is the sum of the total reactive power compensation of the power grid at time t and the reactive power of the generator at time t.

8. A power dispatching device suitable for power generation fluctuations under renewable energy fluctuations, characterized in that, include: Objective function construction module, model construction module, scheduling parameter solving module, and power control module; The objective function construction module is used to construct an objective function with the goal of minimizing the voltage deviation of the converter station. The model building module is used to model the uncertainty of new energy output based on new energy operation data under several different operating scenarios, and build a new energy operation model. Based on voltage data, operation data of various new energy sources, charging and discharging power data of energy storage components, and initial impedance values ​​of energy storage components, a response model for energy storage components is constructed. Based on the DC voltage data, voltage recovery rate data, AC voltage data, and power data of the converter station in the flexible DC system, a voltage security domain constraint is constructed to ensure the voltage stability of the power grid when the output of new energy sources fluctuates. The scheduling parameter solving module is used to solve the new energy operation model, energy storage element response model, voltage safety domain constraint and objective function. When the voltage deviation of the converter station is minimal, it outputs the first power scheduling parameter of the converter station and the second power scheduling parameter of the energy storage. The power control module is used to adjust the power output of the converter station according to the first power scheduling parameter, and to control the charging and discharging power of the energy storage according to the second power scheduling parameter.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a power dispatching method applicable to new energy fluctuations as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute a power dispatching method applicable to new energy fluctuations as described in any one of claims 1 to 7.

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