A wind-solar energy storage system based on simulation optimization

Abstract

The application relates to the technical field of wind-solar energy storage system control, and discloses a wind-solar energy storage system based on simulation optimization, which comprises a control module. The module obtains a power change rate of renewable energy to set frequency response parameters of an energy storage inverter, and monitors a power grid frequency change rate. When the power grid frequency change rate is out of limit, the aforementioned setting logic is interrupted to perform limit support, and all the set parameters are constrained according to an energy storage state of charge before being issued. The application establishes a pre-damping preparation mechanism by responding to a cause of disturbance, that is, a power change rate, instead of a result of disturbance, that is, a frequency deviation, changes the stable effect of the energy storage system from lag compensation to instant suppression, and introduces a global emergency state judgment, so that the distributed energy storage unit has a system-level autonomous cooperative defense capability when responding to different levels of disturbance.

Description

Technical Field

[0001] This invention relates to a wind-solar energy storage system based on simulation optimization, belonging to the field of wind-solar energy storage system control technology. Background Technology

[0002] Currently, large-scale integration of renewable energy sources such as wind and solar power is a recognized technological direction. To cope with their inherent volatility and intermittency, configuring energy storage systems and adopting day-ahead or intraday scheduling plans based on weather forecasts are the mainstream technologies widely used in the industry. Through pre-determined energy storage charging and discharging plans, it effectively mitigates changes in renewable energy power generation and load over time.

[0003] However, with the continuous growth of renewable energy installed capacity, the power grid is no longer facing only slow power changes on an hourly or minute-by-minute basis. Higher frequency and larger amplitude instantaneous power surges are becoming the norm, such as sudden gusts encountered by large wind farms or rapidly moving cumulonimbus clouds over photovoltaic power stations. These real-world scenarios place stringent demands on the dynamic stability of the system. In these scenarios, the inherent technical limitations of traditional dispatching methods that rely on meteorological forecasts at the minute level or above become apparent. Due to the limitations of physical laws, no numerical weather forecast can accurately predict the specific intensity and duration of such local instantaneous disturbances with a precision of seconds or sub-seconds. This results in the failure of forecast-based energy storage dispatching plans at the moment the disturbance occurs.

[0004] To compensate for this deficiency, an intuitive approach is to continuously improve the accuracy of the prediction model and shorten the communication and computation latency. However, this approach does not address the essence of the problem; it remains within a prediction-planning-execution control logic framework, attempting to use a non-real-time, deterministic information domain plan to cope with a rapidly changing, random physical domain reality. An inherent mismatch exists between the two. Another approach is to reserve a large amount of energy storage capacity as backup, ready to cope with the worst-case scenario. However, this directly leads to low utilization of energy storage assets and high operating costs, creating an irreconcilable contradiction between system security and economy. Specifically… Existing technologies suffer from the following shortcomings: 1. Control response lags behind physical disturbances. Current control methods only initiate a response after detecting deviations in grid state variables such as frequency or voltage from the normal range. Essentially, this is a reactive compensation, failing to suppress disturbances at their initial stage. 2. System safety margin relies on economic sacrifices. Large reserves of energy storage designed to ensure dynamic safety remain idle for most of the system's stable operation, resulting in resource waste. 3. Distributed units lack autonomous response capabilities. Each new energy and energy storage unit passively receives remote dispatch commands and cannot proactively provide immediate dynamic support to the grid based on its own and neighboring units' real-time operating status. Therefore, the technical problem this invention aims to solve is how to create a system that does not rely on future predictions but solely on real-time insights into current physical disturbances, enabling the energy storage system to instantly and adaptively adjust its grid support characteristics without sacrificing daily operational economy, thereby effectively mitigating the dynamic stability risks brought about by a high proportion of renewable energy integration. Summary of the Invention

[0005] This invention provides a wind and solar energy storage system based on simulation optimization. Its main purpose is to solve the problem that existing control methods have inherent delays due to their reliance on prediction, which makes it difficult to balance the dynamic safety and operational economy of the system when dealing with instantaneous power surges from new energy sources.

[0006] To achieve the above objectives, the present invention provides a simulation-optimized wind and solar energy storage system, comprising:

[0007] Energy storage unit and control module;

[0008] The control module, associated with one or more renewable energy generation units, is configured to perform the following operations in each control cycle: acquire the output power of the renewable energy generation unit at the grid connection point and calculate its power change rate based on the output power; input the absolute value of the power change rate into a nonlinear gain function to calculate and set a frequency response control parameter for the grid-connected inverter of the energy storage unit;

[0009] Simultaneously, the frequency of the power grid where the energy storage unit is located is obtained and its frequency change rate is calculated. The absolute value of the frequency change rate is compared with a system emergency state threshold stored in the control module. When the absolute value of the frequency change rate exceeds the system emergency state threshold, the aforementioned calculation based on the power change rate is interrupted, and the frequency response control parameter is forcibly set to a limit support value. Furthermore, the state of charge of the energy storage unit is obtained, and a response capability scaling factor is calculated based on the state of charge. The frequency response control parameter to be issued is multiplied by the response capability scaling factor to obtain the final execution parameter and is issued to the grid-connected inverter.

[0010] Preferably, the frequency response control parameter is the damping coefficient of the virtual synchronous machine control algorithm inside the grid-connected inverter; the control module is configured to maintain the damping coefficient at a basic damping value when the absolute value of the power change rate is lower than a first threshold, and when the absolute value of the power change rate is not lower than the first threshold, the damping coefficient is monotonically and nonlinearly increased with the increase of the absolute value of the power change rate through a nonlinear gain function.

[0011] Preferably, the damping coefficient is calculated according to the following formula: ,in, The calculated damping coefficient, Based on the basic damping value, This represents the absolute value of the rate of change of power. It is a nonlinear gain function, whose output value increases as its input value increases, and the growth rate of its output value also increases as its input value increases.

[0012] Preferably, the system emergency threshold is a fixed value in the range of 0.5Hz / s to 2Hz / s; the ultimate support value is the maximum damping coefficient value that the grid-connected inverter can output, which is stored in the control module and corresponds to the model and specifications of the grid-connected inverter.

[0013] Preferably, the response capability scaling factor is a function with a value between 0 and 1 that is associated with the state of charge of the energy storage unit; when the state of charge is within a safe operating range defined by the upper charging threshold and the lower discharging threshold, the value of the response capability scaling factor is 1; when the state of charge exceeds the safe operating range, the value of the response capability scaling factor decreases monotonically as the state of charge moves further away from the safe operating range.

[0014] Preferably, the control module is further configured to perform the following operations: synchronously acquire the effective voltage value of the grid-connected point of the renewable energy generation unit; calculate the time derivative of the power change rate to obtain its steepness, and determine the consistency between the moment when the absolute value of the power change rate exceeds a second threshold and the moment when the effective voltage value synchronously deviates from its rated value by more than 0.05%; based on the steepness exceeding a third threshold and the consistency satisfying the synchronization criterion, classify the disturbance source causing the output power fluctuation as an electrical source disturbance, otherwise classify it as a natural source disturbance; when the disturbance source is classified as an electrical source disturbance, the control module adjusts the frequency response control parameter and simultaneously adjusts one voltage response control parameter of the grid-connected inverter.

[0015] Preferably, the voltage response control parameter is a voltage droop coefficient or a reactive current command; when the disturbance source is classified as an electrical source disturbance, the control module sets the voltage response control parameter to a parameter value that enables the grid-connected inverter to output the maximum reactive power within its rated capacity allowable range.

[0016] Preferably, before inputting the absolute value of the power change rate into the nonlinear gain function, the control module is further configured to perform the following operations: obtain the rated installed capacity of the renewable energy power generation unit and its real-time output power; when the power change rate is positive, normalize the power change rate using the power margin for increased generation calculated from the difference between the rated installed capacity and the real-time output power; when the power change rate is negative, normalize the power change rate using the power margin for reduced generation calculated from the real-time output power to generate a normalized fluctuation signal; wherein, the calculation of the frequency response control parameters is based on the normalized fluctuation signal.

[0017] Preferably, the control module is further configured to perform the following operations during a non-disturbance state when the absolute value of the power change rate is below a stable operating threshold: within a time window of 1 second, calculate the variance of the effective value of the grid-connected voltage as the voltage dispersion, and calculate the total harmonic distortion rate of the effective voltage value; based on the values ​​of the voltage dispersion and the total harmonic distortion rate, query and update the second threshold and the third threshold from a preset two-dimensional lookup table.

[0018] Preferably, the control module is configured to generate and send a status flag signal containing the energy storage unit's identity and its current response capability scaling factor value to the upper-level scheduling system when the value of the response capability scaling factor is less than 0.5.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. By acquiring the output power change rate of renewable energy power generation units in real time, the frequency response parameters of the energy storage system are adjusted before the grid frequency shifts, establishing a control method that responds to disturbances rather than their results. Compared with existing technologies that rely on frequency deviation signals for feedback regulation, this invention transforms the stabilizing effect of the energy storage system from a delayed compensation behavior to a proactive damping preparation. This allows frequency fluctuations caused by sudden changes in renewable energy power to be suppressed in the initial stage by the system's inherent enhanced response stiffness, thus avoiding the large-scale transmission and amplification of disturbance energy in the grid.

[0021] 2. This invention further introduces the monitoring of the grid frequency change rate and uses it as a higher priority judgment criterion. When the system detects that the frequency change rate exceeds the preset value, it will unconditionally interrupt the adjustment logic based on the renewable energy output power change rate and instead execute the extreme support strategy. This mechanism enables each distributed energy storage unit to transcend the limitations of local states and form a unified and coordinated global defense action when facing systemic frequency emergencies caused by non-local renewable energy fluctuations from afar. This solves the problem of cognitive blind spots between distributed control units in local stability and global crisis.

[0022] 3. The analysis of the rate of change of renewable energy output power has been expanded from simple amplitude monitoring to the analysis of its time-series characteristics. Combined with the voltage synchronization changes at the grid connection point, disturbance sources are classified. When it is determined that the disturbance originates from an electrical fault rather than natural fluctuations, the energy storage system will synchronously adjust its voltage response control parameters to provide voltage support. This approach allows the limited capacity of the energy storage inverter to be allocated to the support dimension most needed by the current grid, namely frequency stability or voltage stability, based on the physical nature of the disturbance event. This avoids the situation where a single response strategy fails to meet the actual needs of the system in complex fault scenarios. Attached Figure Description

[0023] Figure 1 This is a logical architecture diagram of the control module of the present invention, which includes a triple collaborative defense mechanism;

[0024] Figure 2 This is a comparison diagram of the response characteristics of adaptive damping and fixed damping under power disturbance of the present invention;

[0025] Figure 3 This is a schematic diagram illustrating the deployment and information interaction of the control core of this invention in a wind-solar-storage integrated system;

[0026] Figure 4 This is the timing diagram of the adaptive control logic based on the power change rate threshold judgment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below. 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.

[0028] This invention provides a simulation-optimized wind-solar energy storage system. Its system architecture includes a control module, one or more associated renewable energy generation units, and an energy storage unit. The control module periodically executes a series of operations based on real-time calculations of local electrical quantities to adjust the response characteristics of the grid-connected inverter of the energy storage unit. In a specific application, the control module is deployed in a step-up substation of a large wind or solar power plant, communicating with the energy storage unit within the substation. Its functional logic consists of a power fluctuation sensing and response parameter adjustment module, a system emergency state judgment and limit support module, and an energy storage physical state constraint module. The data flow and control between these modules are interconnected. The control relationship is as follows: the power fluctuation sensing and response parameter adjustment module obtains the output power data from the grid-connected metering point of the renewable energy generation unit, and calculates a basic control value for adjusting the energy storage frequency response parameter based on this data. At the same time, the system emergency state judgment and limit support module monitors the system frequency in parallel, and when it is determined that the system frequency has changed drastically, it forcibly overrides the output of the aforementioned basic control value and executes a preset limit support strategy. Furthermore, all parameters to be issued generated by these two modules must be verified and adjusted by the energy storage physical state constraint module before being output to the grid-connected inverter, so that the final action is matched with the current physical capacity of the energy storage unit.

[0029] In one specific implementation, the system operates in a large-scale photovoltaic power plant with a rated installed capacity of 500MW, equipped with energy storage units with a rated power of 100MW / 200MWh. In such grids with a high proportion of renewable energy, instantaneous power fluctuations caused by weather are a factor affecting grid frequency stability. To suppress these fluctuations in their initial stages, the control module is configured as a real-time controller with a control cycle of no more than 100 milliseconds. This controller uses a communication protocol to collect instantaneous values ​​of three-phase voltage and current at high frequency from the grid-connected metering point of the photovoltaic power plant, and calculates the real-time output active power of the grid-connected point based on the measured values. Furthermore, the controller obtains the power change rate by performing differential calculations on the power values ​​of two consecutive control cycles. For example, in The measured power was 400MW at any given time. If the measured power is 390MW, then the calculated power is... The absolute value of this rate of change of power 100 MW / s is used as an input signal for the dynamic calculation of subsequent frequency response control parameters. In this way, a control mechanism based on the rate of change of power is established. In one embodiment of this scheme, the frequency response control parameter is specifically the damping coefficient in the Virtual Synchronous Machine (VSG) control algorithm used within the grid-connected inverter of the energy storage unit. This damping coefficient affects the strength of the inverter's response to changes in grid frequency. To ensure that the response strength adapts to the degree of disturbance, the control module will use the absolute value of the acquired power change rate. Input a nonlinear gain function The calculation of this function follows the following relation: ,in, This serves as a base damping value, for example, set to 5 MW / (Hz). Its function is to provide the energy storage system with basic frequency support capability during stable grid operation. (Nonlinear gain function) It is designed as a piecewise function, and its implementation is as follows: a first threshold is set, for example, 10 MW / s, when When the power level is below the first threshold, it is determined to be a normal power fluctuation. ,Right now Maintain at ;when If the value is not lower than the first threshold, then nonlinear calculation is enabled, for example... ,in The gain coefficient is denoted by . Through this design, the damping coefficient increases monotonically and nonlinearly with the increase of the absolute value of the power change rate, and its growth rate also increases accordingly.

[0030] To further improve response accuracy and address the issue of varying disturbance levels represented by the same absolute value of power change for renewable energy units with different capacities or operating states, the control module performs a normalization process before inputting the absolute value of the power change rate into the nonlinear gain function. The procedure is as follows: the controller obtains the rated installed capacity of the renewable energy generation unit. For example, 500MW and its real-time output power When the calculated power change rate When the value is positive, the controller utilizes the power margin, i.e. Normalize the rate of change; when When the value is negative, the power reduction margin is used, i.e. The signal is normalized. For example, for a photovoltaic power plant with a real-time power of 100MW, a rate of change of +20MW / s has a normalized value of 20 / (500-100) = 0.05 / s. For a power plant with a real-time power of 450MW, the same rate of change of +20MW / s has a normalized value of 20 / (500-450) = 0.4 / s. The subsequent calculation of frequency response control parameters will be based on this normalized fluctuation signal.

[0031] Relying on power fluctuation information from local renewable energy sources may lead to insufficient response of energy storage units when faced with systemic frequency events originating from distant locations. To address this, the control module, while executing the aforementioned logic, also monitors the frequency of the power grid where the energy storage unit is located in parallel and calculates its frequency change rate. The controller internally stores a system emergency threshold, which can be set to a fixed value within the range of 0.5Hz / s to 2Hz / s, for example, 1Hz / s. In each control cycle, the controller compares the absolute value of the calculated frequency change rate with this threshold. When the absolute value exceeds 1Hz / s, all the aforementioned calculation logic based on the power change rate is interrupted, and the frequency response control parameters are changed. It is forcibly set to a limit support value, which is the maximum damping coefficient value corresponding to the model and specifications of the grid-connected inverter stored in the control module. The purpose of this mechanism is to enable each energy storage unit to perform unified limit support control when it is determined that the system frequency has changed drastically, without being affected by its local power fluctuation state.

[0032] The issuance of any control command must consider the physical feasibility of the execution unit. To avoid issuing commands that exceed the capabilities of the energy storage battery's current state of charge, the control module executes a constraint based on the energy storage's state of charge before issuing any calculated frequency response control parameters. Specifically, the controller obtains the energy storage unit's state of charge in real time through the battery management system. and based on this Calculate a response capability scaling factor, which is a factor that is proportional to... The associated function takes values ​​between 0 and 1; when When within a safe operating range defined by an upper charge threshold (e.g., 90%) and a lower discharge threshold (e.g., 20%), the response capability scaling factor is 1; when When outside the safe working range, for example Once it reaches 95%, its ability to continue charging decreases, and at this point, the value of the response capability scaling factor will change accordingly. As the distance from the safe operating range increases, the frequency response control parameters to be issued will be multiplied by the response capability scaling factor to obtain the final execution parameters before being issued to the grid-connected inverter. In addition, when the value of the response capability scaling factor is lower than a preset threshold, such as 0.5, the controller will also generate and send a status flag signal containing the energy storage unit's identity and its current response capability scaling factor value to the upper-level dispatching system.

[0033] To enable the capacity of the energy storage inverter to be allocated across two dimensions—frequency stability and voltage stability—this invention also introduces a disturbance source classification mechanism. To distinguish the physical source of power fluctuations solely based on local electrical quantities, the control module is configured to synchronously acquire the effective voltage value of the renewable energy generation unit's grid connection point; and to calculate the time derivative of the power change rate, i.e. The steepness of the slope is determined by the following steps: First, the absolute value of the power change rate exceeds the second threshold, and second, the consistency between the moment when the effective voltage value deviates synchronously from its rated value by more than 0.05%. Based on the steepness exceeding the third threshold and the consistency satisfying the synchronization criterion, the disturbance source causing output power fluctuations is classified as an electrical source disturbance; otherwise, it is classified as a natural source disturbance. When the disturbance source is classified as an electrical source disturbance, the control module adjusts the frequency response control parameters and simultaneously adjusts a voltage response control parameter of the grid-connected inverter, such as the voltage droop coefficient or reactive current command, setting it to the parameter value that allows the grid-connected inverter to output the maximum reactive power within its rated capacity. To ensure the above disturbance source classification mechanism is effective under different grid background conditions... To ensure accuracy, the solution also includes a procedure for dynamically adjusting the classification threshold. This addresses the issue that a fixed threshold might lead to misjudgments or missed judgments when the power grid is already in a state of low voltage or harmonic pollution. To this end, the control module is configured to perform the following operations during a non-disturbance state when the absolute value of the power change rate is below a stable operating threshold: within a time window of 1 second, calculate the variance of the effective voltage value at the grid connection point as the voltage dispersion, and calculate the total harmonic distortion rate of the effective voltage value; based on the values ​​of the voltage dispersion and the total harmonic distortion rate, query and update the aforementioned second and third thresholds used for disturbance source classification from a preset two-dimensional lookup table. Through this mechanism, the system's identification scale can adapt to the real-time state of the power grid.

[0034] Example 1: In the operation of a large wind farm and its associated energy storage system with a grid-connected active power baseline of 400MW, the wind speed is stable in the initial stage, and the output power of the wind farm fluctuates slightly around 400MW. At this time, the power change rate calculated by the control module in the system in each control cycle is... All are below the preset first threshold of 10MW / s. Correspondingly, the virtual synchronous machine damping coefficient of the grid-connected inverter of the energy storage unit is... Maintained at the basic damping value The energy storage unit is executing economic commands issued by the upper-level dispatch system to perform low-power charging. The system's operation prioritizes dispatch economy, while its stability capability in response to sudden disturbances is in a standby state through internal control logic, rather than by continuously occupying reserve power. Subsequently, a strong gust front that was not predicted by the meteorological system passed through, causing the wind farm's active power output to drop rapidly from 400MW to 100MW within 3 seconds. Within the first 100ms period after the disturbance occurred, the control module calculated a power change rate of -100MW / s. Its absolute value is no less than the first threshold, and the control module then uses a nonlinear gain function. Calculate a damping increment and set the total damping coefficient. Instantly adjusting to a high level, the energy storage inverter, before the grid frequency has significantly shifted, has already switched from economic operation mode to high-rigidity support mode based on the calculation results of the disturbance source information, and has begun high-power discharge to offset the rapid decline in wind power output. This working mode does not rely on the prediction of disturbances, but directly calculates and responds to the physical quantity when the disturbance occurs, namely the power change rate, changing the control method from lagging planning based on information domain prediction to real-time adjustment based on physical domain measurement.

[0035] Two seconds after the aforementioned gust disturbance occurred, the regional power grid where the wind farm is located went offline due to a fault in a large generator unit at a distant location, causing a power deficit across the entire grid. This resulted in the system frequency beginning to drop rapidly. At this time, although the wind farm's power was still decreasing, its rate of change... The power has already declined from its initial peak. If only the power change rate signal is relied upon, the support strength of energy storage will be weakened. However, the system emergency state judgment and ultimate support module in this solution calculates the frequency change rate by monitoring the local power grid frequency. The system emergency threshold of 1 Hz / s has been exceeded. This module's decision logic has higher priority; it immediately interrupts the calculation based on the power change rate and changes the damping coefficient. The system is forcibly set to the maximum support value that the inverter can support; the coexistence of the power fluctuation sensing and response parameter adjustment module and the system emergency state judgment and limit support module enables the former to provide rapid adjustment capabilities to cope with local disturbances, while the latter provides a deterministic support lower limit to cope with global system risks; throughout the dynamic process, the control module also continuously monitors the state of charge of the energy storage unit. In the early stages of the process, energy storage With a response scaling factor of 1 and a 60% efficiency, the calculated control parameters are fully executed. As the energy storage continues to discharge at high power, when its... When the discharge threshold drops to 20%, the response scaling factor begins to scale from 1. The damping factor decreases smoothly as the voltage drops further, and the damping factor sent to the inverter is the product of the ultimate support value and the scaling factor. This constraint based on the physical state of energy storage ensures that the system's control commands are always within its physical feasible domain, avoiding secondary disturbances that may be caused by issuing commands that exceed the battery's discharge capacity. Ultimately, under the combined effect of multiple control mechanisms, the lowest point of the grid frequency is maintained above the safe threshold, and the system recovers stability after experiencing the dynamic process, avoiding the occurrence of regional low-frequency load shedding.

[0036] Example 2: To quantitatively verify the effectiveness of the technical solution of this invention in suppressing the power surge of renewable energy grid connection, a power system dynamic simulation platform was built. This platform is based on a standard second-order generator model and a VSG inverter average value model. Its main components include: an equivalent synchronous generator set with a moment of inertia H of 5s to simulate the main grid characteristics; a constant impedance load; a renewable energy generation unit with a rated capacity of 100MW; and an energy storage unit with a rated power of 20MW / 40MWh. The reference frequency of the simulation platform was set to 50Hz, and the data acquisition step size for all electrical and control parameters was set to 1ms. To compare the effects of different control methods, a control group and the sample group of this invention were set up. The energy storage inverter in the control group adopted a control method with primary frequency regulation capability, and its virtual synchronous machine damping coefficient was set to a fixed value of 10MW / Hz. The sample group of this invention was configured with all the control logic in the specific implementation method, including the control logic based on the power change rate. Nonlinear adaptive damping adjustment, and higher priority frequency-rate-of-change-based damping adjustment. The ultimate support mechanism simulated the disturbance event in the experiment. When the simulation reached the 2nd second, the output power of the renewable energy power generation unit decreased linearly from 100MW to 40MW within 1 second, resulting in a power change rate of -60MW / s.

[0037] Under the same disturbance conditions, simulations were performed on two sample groups. During the experiment, the frequency response of the energy storage inverter in the control group from the start of the disturbance to system stability was determined by its fixed damping coefficient. The control module of the sample group of this invention monitored a frequency of 60 MW / s at the initial moment of the disturbance. Based on the nonlinear gain function relationship, the damping coefficient is instantaneously... The baseline value of 5 MW / Hz was increased to a level higher than the fixed value of the control group, thereby mobilizing the energy storage power to provide support during the frequency drop phase. The quantitative comparison data of the key performance indicators of the two sample groups under different disturbance intensities are shown in Table 1.

[0038] Table 1: Comparison of system frequency response indices for two sample groups under different disturbance intensities.

[0039] Disturbance intensity (power drop %) Sample group category Maximum frequency deviation (Hz) Lowest frequency point (Hz) Frequency settling time (s) 40 control group -0.18 49.82 8.5 40 Sample of the present invention -0.10 49.90 5.2 60 control group -0.28 49.72 9.8 60 Sample of the present invention -0.15 49.85 6.1 80 control group -0.39 49.61 11.2 80 Sample of the present invention -0.21 49.79 7.5

[0040] Simulation data show that, when dealing with the same power disturbance, the maximum frequency deviation and frequency stabilization time of the sample group of the present invention are both smaller than those of the control group. The data trend in Table 1 further shows that as the disturbance intensity increases, the performance difference between the sample group of the present invention and the control group increases. The reason for this phenomenon is that the response intensity of the sample group of the present invention is related to the severity of the disturbance source and can match the corresponding support when the disturbance increases, while the fixed parameters of the control group limit its response capability. The test results confirm that the technical solution of the present invention has an improving effect on the dynamic frequency stability of power systems with a high proportion of renewable energy.

[0041] Example 3: This example combines Figures 1 to 4 This describes a simulation-optimized wind-solar energy storage system, such as... Figure 1 As shown, the control module receives the output power and voltage RMS values ​​from the renewable energy generation unit, the grid frequency and frequency change rate from the grid, and the state of charge (SoC) from the energy storage unit as inputs. It integrates three core functional modules: a power fluctuation sensing and response parameter adjustment module, a system emergency state judgment and limit support module, and an energy storage physical state constraint module. The power fluctuation sensing and response parameter adjustment module establishes a pre-damping preparation mechanism and generates basic frequency response control parameters based on the response to the cause of the disturbance, i.e., power change. The system emergency state judgment and limit support module monitors the global state and executes forced interruption and overwrite when necessary, demonstrating autonomous and collaborative defense capabilities. All control parameters must be verified by the energy storage physical state constraint module before being finally issued to ensure that the control command is within the physical feasible domain and finally outputs the final execution parameters to the energy storage unit containing the grid-connected inverter.

[0042] like Figure 2 As shown in the figure, the horizontal axis represents time in seconds (s), and the vertical axis on the left represents the rate of change of power. The units are MW / s, and the right vertical axis represents the damping coefficient D, with units of MW / Hz. As can be seen from the graph, the dashed line in the fixed damping coefficient graph maintains a constant value of 10 MW / Hz throughout the process, while the solid line in the adaptive damping coefficient graph closely follows the power change rate. The changes shown by the dots and lines in the diagram are dynamically adjusted. As it begins to increase dramatically at the second second, the adaptive damping coefficient also increases rapidly and nonlinearly. When it reaches its peak speed of approximately 90 MW / s, it simultaneously reaches a peak speed of approximately 22 MW / Hz, after which it continues to rise. The response intensity decreased as the frequency dropped and eventually recovered to the baseline value of 5 MW / Hz. This process demonstrates a precise match between the response intensity and the degree of disturbance.

[0043] like Figure 3 As shown, the control core is typically deployed in the step-up substation of a wind farm or photovoltaic power station. It consists of real-time controller hardware and control module software running on it. The control core obtains power, voltage, and grid frequency data from the renewable energy generation units and grid-connected metering points, respectively, and communicates bidirectionally with the energy storage unit area to receive energy storage charge status feedback and issue final execution parameters. At the same time, the control core can also send status flag signals to the upper-level dispatching system, thus forming a complete control system with a closed-loop information flow and the ability to make local autonomous decisions and coordinate with the upper-level system.

[0044] like Figure 4 As shown, the process is executed cyclically with a control cycle of 100ms. It begins with the control module acquiring the output power P data from the renewable energy generation unit and then calculating its rate of change. Then it enters a conditional branch, if the absolute value of the power change rate is... If the resistance is less than a preset first threshold, the power fluctuation sensing module instructs the grid-connected inverter to maintain the base damping value. ,like If the value is not less than the first threshold, normalization processing, nonlinear gain function calculation, and adaptive damping coefficient are performed sequentially. Before the coefficient is sent out, the response capability scaling factor needs to be calculated in conjunction with the SoC value obtained from the energy storage unit to generate the final execution parameters and send them to the grid-connected inverter to perform the corresponding frequency support control.

[0045] Example 4: To deploy the control system of this invention in a photovoltaic power plant with a rated capacity of 500MW, a parameter calibration procedure needs to be performed to match the adjustable parameters in the control module with the operating characteristics of the specific power plant and the grid conditions at the grid connection point. This procedure aims to set the first threshold for the power change rate in the control logic and the gain coefficient in the nonlinear gain function. Determine numerical values ​​with clear engineering basis.

[0046] The first step in this calibration procedure is to determine the first threshold for the power change rate. This threshold distinguishes between normal power fluctuations at the power plant and significant disturbances requiring energy storage system intervention. To this end, historical output power data for the 500MW photovoltaic power plant was collected over 30 consecutive operating days at a sampling frequency of 10Hz. Based on this dataset, the power change rate between all adjacent sampling points was calculated. The data was analyzed using statistical probability distribution. The results showed that 99.5% of the absolute values ​​of the power change rate were below 12 MW / s. Events exceeding this value were marked as abnormal operating conditions in the operation log. Therefore, to enable the energy storage system to respond to statistically low-probability significant events, the first threshold was set at 12 MW / s. The second step of the procedure is to determine the nonlinear gain function. Gain coefficient in Therefore, using the aforementioned simulation platform, the photovoltaic power plant model parameters were set to 500MW, and a power change step disturbance of -100MW / s was applied. Subsequently, a set of scanning simulation experiments were conducted, in which the gain coefficient was... The value starts at 0.001 and increases in increments of 0.0005 to 0.01. The maximum system frequency deviation and whether there is oscillation in the energy storage output power are recorded in each simulation. Experimental data shows that when... When the value is greater than 0.005, the energy storage output power oscillates, while... When the value is 0.0045, the system achieves the minimum frequency deviation without oscillation; therefore, the gain coefficient... The value was determined to be 0.0045; the third step of the procedure is to set the system emergency state threshold. This threshold is a boundary condition associated with the power grid safety procedure, and its setting is based on the regional power grid operation guidelines followed by the grid connection point of the photovoltaic power station. These guidelines stipulate that when the absolute value of the power grid frequency change rate is... When the speed exceeds 1 Hz / s, the grid-connected unit must enter the emergency support state. Therefore, the system emergency state threshold in the control module is set to 1 Hz / s. By executing the above complete calibration procedure, a set of parameters adapted to a specific site is obtained, and these parameters are written into the configuration file of the control module.

[0047] Example 5: The dynamic adaptation of the second and third thresholds for disturbance source classification is achieved through a two-dimensional lookup table generated before the control module is deployed. The generation procedure of this lookup table utilizes the aforementioned simulation platform and is completed by applying a systematic parameter scan to the power grid background state. Specifically, two independent variables are set: voltage dispersion and total harmonic distortion (THD). The former is simulated by injecting white noise of different amplitudes into the power grid voltage, while the latter is simulated by injecting harmonic current sources of different orders. At each operating point composed of voltage dispersion and THD values, characteristic natural source disturbances and electrical source disturbances are superimposed respectively. The classification accuracy is used as the evaluation index. Through iterative calculation, the combination of the second and third thresholds that can achieve a preset index of classification accuracy under this specific power grid background state is determined. All operating points and their corresponding threshold combinations are stored in a data matrix, which is fixed in the non-volatile memory of the control module as a two-dimensional lookup table.

[0048] When the communication link between the control module and its superior scheduling system is interrupted, the control module is configured to enter an autonomous operation mode. In this mode, all functions that rely on instructions from the superior system, such as performing economic scheduling, will be suspended. Simultaneously, all local core control logic that does not rely on external communication, including the rate of change of output power of local renewable energy generation units, will also be suspended. Real-time calculation and response to power grid frequency change rate Monitoring and ultimate support assessment, and based on the state of charge of energy storage The output constraints remain in operation. The control module performs calculations based solely on locally collected electrical quantities and outputs corresponding control parameters to support the dynamic stability of the power grid. Only after communication with the upper-level dispatch system is restored will it switch back to accepting external commands.

[0049] Example 6: Before each operation of the control module, its internal program is set to first execute a standardized input signal quality self-check procedure. This procedure aims to verify the validity of the data from the power, voltage, and frequency measurement channels. Specifically, before entering the active control state, the control module first collects a 5-second period of grid electrical quantity data and calculates the variance of the grid-connected power signal and the variance of the grid frequency signal within this time period. Then, it compares the two calculated variance values ​​with the corresponding maximum permissible noise variance thresholds for the power signal and frequency signal stored internally in the module. If both calculated values ​​are lower than their corresponding thresholds, the input signal quality is deemed qualified, and the control module enters normal operation. If any calculated value is not lower than its corresponding threshold, the corresponding channel is deemed to have data anomalies, and the control module sends a sensor data quality alarm to the background monitoring system and remains in standby mode until the fault is resolved.

[0050] The classification of disturbance sources is based on a set of deterministic algorithmic steps, whereby the control module detects the absolute value of the power change rate. When the second threshold, updated via a two-dimensional lookup table, is exceeded, a classification decision window is activated. During this window, the control module performs two decisions in parallel. The first decision is to calculate the absolute value of the time derivative of the power change rate, i.e., the steepness. The system compares the threshold value with a third threshold value updated via a two-dimensional lookup table. If the former is not lower than the latter, a Boolean steepness flag is set to true. The second criterion is to monitor whether the effective voltage value deviates from its rated value by more than 0.05% within the same window period. If such a deviation occurs, a Boolean synchronization flag is set to true. Finally, the control module performs a logical AND operation on these two flags. If and only if both the steepness flag and the synchronization flag are true, the current disturbance source is classified as an electrical source disturbance. Otherwise, it is classified as a natural source disturbance, and subsequent control parameter adjustments are performed based on this classification result.

[0051] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A wind-solar energy storage system based on simulation optimization, characterized in that, include: Energy storage unit and control module; The control module, associated with one or more renewable energy generation units, is configured to perform the following operations in each control cycle: acquire the output power of the renewable energy generation unit at the grid connection point and calculate its power change rate based on the output power; input the absolute value of the power change rate into a nonlinear gain function to calculate and set a frequency response control parameter for the grid-connected inverter of the energy storage unit; Simultaneously, the frequency of the power grid where the energy storage unit is located is obtained and its frequency change rate is calculated; the absolute value of the frequency change rate is compared with a system emergency state threshold stored in the control module. When the absolute value of the frequency change rate exceeds the system emergency state threshold, the aforementioned calculation based on the power change rate is interrupted, and the frequency response control parameter is forcibly set to a limit support value; furthermore, the state of charge of the energy storage unit is obtained, and a response capability scaling factor is calculated based on the state of charge. The frequency response control parameter to be issued is multiplied by the response capability scaling factor to obtain the final execution parameter and issued to the grid-connected inverter. Among them, the frequency response control parameter is the damping coefficient of the virtual synchronous machine control algorithm inside the grid-connected inverter; the control module is configured to maintain the damping coefficient at a basic damping value when the absolute value of the power change rate is lower than a first threshold, and when the absolute value of the power change rate is not lower than the first threshold, the damping coefficient is monotonically and nonlinearly increased with the increase of the absolute value of the power change rate through a nonlinear gain function. The damping coefficient is calculated according to the following formula: ,in, The calculated damping coefficient, Based on the basic damping value, This represents the absolute value of the rate of change of power. It is a nonlinear gain function, whose output value increases with the increase of its input value, and the growth rate of its output value also increases with the increase of its input value. The response capability scaling factor is a function between 0 and 1 that is associated with the state of charge of the energy storage unit. When the state of charge is within a safe operating range defined by the upper charging threshold and the lower discharging threshold, the value of the response capability scaling factor is 1. When the state of charge exceeds the safe operating range, the value of the response capability scaling factor decreases monotonically as the state of charge moves further away from the safe operating range.

2. The wind-solar energy storage system based on simulation optimization according to claim 1, characterized in that, The system emergency threshold is a fixed value within the range of 0.5Hz / s to 2Hz / s; the ultimate support value is the maximum damping coefficient value that the grid-connected inverter can output, which is stored in the control module and corresponds to the model and specifications of the grid-connected inverter.

3. The wind-solar energy storage system based on simulation optimization according to claim 1, characterized in that, The control module is also configured to perform the following operations: synchronously acquire the effective voltage value of the grid-connected point of the renewable energy generation unit; calculate the time derivative of the power change rate to obtain its steepness, and determine the consistency between the moment when the absolute value of the power change rate exceeds the second threshold and the moment when the effective voltage value deviates from its rated value by more than 0.05%; based on the steepness exceeding the third threshold and the consistency satisfying the synchronization criterion, classify the disturbance source causing the output power fluctuation as an electrical source disturbance, otherwise classify it as a natural source disturbance; when the disturbance source is classified as an electrical source disturbance, the control module adjusts the frequency response control parameter and simultaneously adjusts one of the voltage response control parameters of the grid-connected inverter.

4. A wind-solar energy storage system based on simulation optimization according to claim 3, characterized in that, The voltage response control parameter is either the voltage droop factor or the reactive current command. When the disturbance source is classified as an electrical source disturbance, the control module sets the voltage response control parameter to the value that enables the grid-connected inverter to output the maximum reactive power within its rated capacity.

5. A wind-solar energy storage system based on simulation optimization according to claim 1, characterized in that, Before inputting the absolute value of the power change rate into the nonlinear gain function, the control module is configured to perform the following operations: obtain the rated installed capacity and real-time output power of the renewable energy generation unit; when the power change rate is positive, normalize the power change rate using the power margin for increased generation calculated from the difference between the rated installed capacity and the real-time output power; when the power change rate is negative, normalize the power change rate using the power margin for reduced generation calculated from the real-time output power to generate a normalized fluctuation signal; wherein, the calculation of the frequency response control parameters is based on the normalized fluctuation signal.

6. A wind-solar energy storage system based on simulation optimization according to claim 3, characterized in that, The control module is also configured to perform the following operations during a non-disturbance state when the absolute value of the power change rate is below a stable operating threshold: within a time window of 1 second, calculate the variance of the effective value of the grid-connected voltage as the voltage dispersion, and calculate the total harmonic distortion rate of the effective voltage value; based on the values ​​of the voltage dispersion and the total harmonic distortion rate, query and update the second and third thresholds from a preset two-dimensional lookup table.

7. A wind-solar energy storage system based on simulation optimization according to claim 1, characterized in that, The control module is configured to generate and send a status flag signal containing the energy storage unit's identity and its current response capability scaling factor value to the upper-level scheduling system when the response capability scaling factor value is below 0.5.

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