Power balance method and related device considering new energy consumption

Abstract

The application relates to the technical field of power dispatch balancing, in particular to a power system power balancing method considering new energy consumption and related equipment, which comprises the following steps: acquiring power grid frequency variation rates of each new energy station grid-connected point at each collection time and actual new energy output data of each new energy station grid-connected point, obtaining high-permeability imbalance indexes of each collection time of the new energy station grid-connected point by the fluctuation degree of the new energy output data of the new energy station grid-connected point and in combination with the instability degree of the power grid frequency variation rate data; acquiring hybrid energy storage misdispatching of each collection time of the new energy station grid-connected point according to the variation trend and variation fluctuation of all high-permeability imbalance indexes before each collection time; and performing hierarchical coordination control by using the hybrid energy storage misdispatching and a joint risk threshold. The application can improve the power dispatch balancing precision of a power system.

Description

Technical Field

[0001] This application relates to the field of power dispatch and balancing technology, specifically to power system balancing methods and related equipment that take into account the consumption of new energy sources. Background Technology

[0002] Traditional power systems primarily rely on controllable thermal and hydropower sources to track load changes and achieve real-time balance. However, renewable energy sources such as wind and solar power exhibit significant volatility, intermittency, and uncertainty, making their output difficult to predict accurately and control proactively. Furthermore, their output often mismatches with load demand in time, necessitating substantial and rapid adjustment resources to fill power gaps and ensure power supply security when renewable energy output drops sharply. Against this backdrop, intelligent dispatch power systems, through multi-dimensional and flexible resource coordination, effectively mitigate the impact of renewable energy fluctuations, ensuring real-time power balance and frequency stability even with a high proportion of renewable energy integration, and guaranteeing power supply resilience under extreme weather conditions.

[0003] When considering the power balance of intelligent dispatch power systems for renewable energy consumption, the key issue of insufficient total flexible adjustment resources and limited response characteristics stems from the drastic fluctuations in wind and solar power generation. Intelligent dispatch power systems urgently need a large amount of flexible resources with millisecond to minute-level rapid response and wide-range adjustment capabilities to instantly fill gaps in renewable energy output or absorb sudden surges in power. However, existing resource pools are insufficient in terms of both total quantity and response speed to match the surge in demand brought about by the rapid increase in renewable energy penetration. Existing technological solutions for renewable energy consumption involve deploying grid-connected large-scale electrochemical energy storage systems. These systems achieve rapid energy interaction with the grid through power electronic converters, instantly releasing stored energy when renewable energy output suddenly drops, effectively suppressing minute-level or even second-level fluctuations. However, when addressing insufficient adjustment capabilities, frequent deep charging and discharging significantly accelerates the capacity decay and aging of lithium-ion batteries, drastically shortening their actual lifespan and limiting their application potential in high-frequency, high-intensity adjustment scenarios. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a power system power balance method and related equipment that considers the integration of new energy sources. The specific technical solution adopted is as follows:

[0005] This application provides a power system power balance method that takes into account the consumption of new energy sources, including the following steps:

[0006] The grid frequency change rate and the actual power output data of each renewable energy power station at each data collection point are obtained at each data collection time, and the grid frequency change rate sequence and renewable energy power output sequence are obtained.

[0007] By analyzing the fluctuation of renewable energy output data at renewable energy power plant grid connection points and combining it with the instability of grid frequency change rate data, a high penetration imbalance index is obtained at each data collection time of renewable energy power plant grid connection points.

[0008] Based on the changing trends and fluctuations of all high-permeability imbalance indices before each collection time, the hybrid energy storage out-of-control situation at each collection time of the new energy power station grid connection point is obtained.

[0009] Layered coordinated control is achieved by utilizing the joint risk threshold of hybrid energy storage failure dispatch.

[0010] Preferably, the grid frequency change rate and actual power output data of each new energy power station at all previous data collection times are sorted in ascending order of time to obtain the grid frequency change rate sequence and new energy power output sequence of each new energy power station at each data collection time.

[0011] Preferably, the method for obtaining the high penetration imbalance index at each data collection point of the new energy power station is as follows:

[0012] In the formula, A is the high penetration imbalance index at the current data collection time of the new energy power plant grid connection point, D is the fluctuation amplitude of the output power at the current data collection time of the new energy power plant grid connection point, and P is the instability of the grid frequency change rate at the current data collection time of the new energy power plant grid connection point.

[0013] Preferably, the new energy power output sequence at the current collection time of the new energy power plant grid connection point is used as input, and the wavelet decomposition algorithm is used to output the detail coefficients at each scale. The maximum absolute value of all detail coefficients under the maximum decomposition level is calculated and used as the fluctuation amplitude of the output power at the current collection time of the new energy power plant grid connection point.

[0014] Preferably, the grid frequency change rate sequence at the current data collection time of the new energy power plant grid connection point is used as input, the standard deviation is calculated using a sliding window, the standard deviation of all data in each sliding window is output, and the mean of the standard deviations of all sliding windows is used as the instability of the grid frequency change rate at the current data collection time of the new energy power plant grid connection point.

[0015] Preferably, the method for obtaining the hybrid energy storage out-of-dispatch data at each data collection time of the new energy power station grid connection point is as follows:

[0016] In the formula, B represents the hybrid energy storage outage at the current data collection time of the new energy power station's grid connection point. G is the mean of the high penetration imbalance index after smoothing the penetration imbalance sequence at the current time of data collection at the grid connection point of the new energy power station using an exponentially weighted moving average algorithm. G is the fluctuation coefficient of the penetration imbalance sequence at the current time of data collection at the grid connection point of the new energy power station.

[0017] Preferably, the mean of the high permeability imbalance index is output using the permeability imbalance sequence at the current time of data collection at the grid connection point of the new energy power station as input and the exponential weighted moving average algorithm.

[0018] Preferably, the peak and valley values ​​of the penetration imbalance sequence at the current collection time of the new energy power station grid connection point are extracted, and the difference between the maximum peak value and the minimum valley value is used as the fluctuation coefficient of the penetration imbalance sequence at the current collection time of the new energy power station grid connection point.

[0019] Preferably, the hierarchical coordinated control using a joint risk threshold for hybrid energy storage failure further includes:

[0020] Preset low-risk threshold and high risk threshold ,when At that time, a strategy of responding to millisecond to second-level fluctuations in the supercapacitor response system is adopted; when When a hybrid coordination operation strategy is adopted, it is used; when At that time, a lithium battery limit protection strategy is adopted.

[0021] This application also provides a novel power system power balance related device that considers the consumption of new energy sources, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the novel power system power balance method that considers the consumption of new energy sources described above.

[0022] As can be seen from the above, the novel power system power balancing method and related equipment considering the consumption of new energy provided in this application have at least the following beneficial effects:

[0023] This application addresses the problem of power imbalance and frequency instability caused by renewable energy fluctuations. It employs wavelet decomposition combined with sliding window standard deviation time quantification to assess the severity of minute-level fluctuations in renewable energy and the frequency instability risk of intelligent dispatch power systems, thus solving the problem that traditional methods cannot coordinate the assessment of power deficits and the vulnerability of intelligent dispatch power systems.

[0024] Furthermore, to address the contradiction between regulating resource lifetime depletion and real-time balance, an exponentially weighted moving average combined with sliding peak-valley detection is adopted. By fusing the data through the sigmoid function, a hybrid energy storage fail-dispatch is generated to eliminate short-term noise interference and comprehensively reflect the long-term pressure and sudden risks of the intelligent dispatch power system, revealing the comprehensive vulnerability in high-penetration scenarios.

[0025] This application employs a hybrid energy storage-based dynamic triggering hierarchical control strategy to enable the intelligent dispatch power system to utilize supercapacitors only to handle millisecond-level fluctuations during low-risk conditions, avoiding ineffective lithium battery cycling; during medium-risk conditions, supercapacitors smooth out second-level fluctuations while lithium batteries handle minute-level smooth adjustments; and during high-risk conditions, supercapacitors operate at full capacity while lithium batteries provide power-limiting protection. This approach reduces the frequency of deep charging and discharging of lithium batteries while ensuring real-time power balance, thereby improving the economy and operational resilience of the intelligent dispatch power system. Attached Figure Description

[0026] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating the steps of the power system power balance method that takes into account the consumption of new energy sources, provided in this application. Detailed Implementation

[0028] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation methods, structures, features, and effects of the novel power system power balancing method and related equipment considering the consumption of new energy sources proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0029] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0030] The following, in conjunction with the accompanying drawings, details the specific scheme of the power system power balance method and related equipment that takes into account the consumption of new energy sources provided in this application.

[0031] Please see Figure 1 It illustrates a flowchart of a power system power balance method considering renewable energy consumption according to an embodiment of this application, including the following steps:

[0032] Step 1: Obtain the grid frequency change rate and the actual power output data of each renewable energy power station at each data collection time, and obtain the grid frequency change rate sequence and renewable energy power output sequence.

[0033] To construct a power balance system that considers the consumption of new energy sources, this embodiment establishes a collaborative intelligent dispatch power system architecture consisting of a sensing layer, a transmission layer, a control layer, and an execution layer, and deploys the following core equipment:

[0034] The sensing layer devices include:

[0035] High-precision synchronous phasor measurement device (PMU): Deployed at the grid connection point of new energy power plants, it collects grid frequency change rate and voltage phase data in real time with millisecond-level accuracy, and accurately senses the instantaneous power imbalance state of the intelligent dispatch power system;

[0036] New energy power station data acquisition module: integrated into the photovoltaic inverter group control system and the wind turbine SCADA system, it collects the actual output curve of new energy with a resolution of seconds, which is used to track the original fluctuation characteristics of wind and solar power.

[0037] Hybrid energy storage battery management system (BMS) sensors: Built into supercapacitor and lithium battery energy storage units, they collect key intrinsic parameters such as supercapacitor state of charge (SOC), lithium battery state of health (SOH), and temperature in real time, and dynamically assess and adjust the available capacity of resources.

[0038] Transmission and edge processing layer devices: mainly edge computing gateways, deployed at site or regional nodes, receiving and processing multi-source heterogeneous data from the sensing layer. They perform core preprocessing operations.

[0039] Execution layer equipment: mainly includes hybrid energy storage systems (HESS), consisting of supercapacitors (SC) and lithium-ion batteries (LIB). Supercapacitors provide rapid power support at millisecond to second levels, with high rates and long cycle life; lithium batteries provide energy regulation at minute levels and with large capacity.

[0040] Control Layer: The core is the intelligent dispatch power system balance control center, which receives the multidimensional dataset processed by the edge gateway and runs the core algorithms for subsequent steps.

[0041] For each energy plant grid connection point, in this embodiment, a high-precision PMU synchronous measurement device is installed at the new energy plant grid connection point to collect grid frequency change rate data and perceive the power imbalance status of the intelligent dispatch power system in real time. The specific process of collecting grid frequency change rate data is existing technology, and this embodiment does not impose any special restrictions on it.

[0042] Data acquisition modules are deployed in the photovoltaic inverter group control system and wind turbine SCADA system at the grid connection point of the new energy power station to collect second-level actual power output data of new energy and accurately track the fluctuation characteristics of wind and solar power.

[0043] All sensor data acquisition frequencies are set to 100Hz. All data undergoes time alignment processing via an edge computing gateway. Min-Max normalization is performed on each type of data, and frequency abrupt noise points are removed based on the Laida criterion to generate a synchronized, high-precision time-series dataset. For each acquisition moment, all data acquired at all previous acquisition moments are sorted in ascending chronological order to obtain the time-series sequence of each data type, providing core input for constructing dynamic adjustment demand indicators. In this embodiment, the grid frequency change rate sequence and the renewable energy output sequence of each renewable energy power station's grid connection point are obtained at each acquisition moment.

[0044] Step 2: By analyzing the fluctuation of renewable energy output data at renewable energy power plant grid connection points and combining it with the instability of grid frequency change rate data, the high penetration imbalance index at each data collection time of renewable energy power plant grid connection points is obtained.

[0045] Due to the significant volatility and intermittency of new energy sources, coupled with the rate of change in power output forecasts, intelligent dispatch power systems frequently face instantaneous power shortages or surpluses during operation, leading to rapid frequency fluctuations or even instability risks. This dynamic imbalance may cause frequency exceedances to trigger protection devices, voltage drops to affect power quality, or force traditional generating units to frequently adjust, exacerbating equipment losses.

[0046] Therefore, taking the renewable energy output sequence at the current time of data collection at the renewable energy power plant grid connection point as an example, the renewable energy output sequence is used as input, and wavelet decomposition algorithm is used. The wavelet basis function is set to db4 to effectively capture the local abrupt change features in the power signal. The decomposition level is set to 3 levels to accurately extract the fluctuation components at this scale. After wavelet decomposition, the detail coefficients of each scale are output. For all detail coefficients of the third level, which represents minute-level fluctuations, the maximum absolute value of the detail coefficients is counted. The maximum value is recorded as the fluctuation amplitude D of the output power, thus reflecting the maximum minute-level fluctuation amplitude of renewable energy output within this time window. The larger the value, the more severe the fluctuation of renewable energy output at the minute scale. The smart dispatch power system needs to reserve more instantaneous adjustment capacity to cope with the risk of power shortage.

[0047] Furthermore, for the grid frequency change rate sequence at the current data collection time of the new energy power plant grid connection point, this embodiment uses the grid frequency change rate sequence as input and a sliding window is used to calculate the standard deviation. The sliding window is set to 10 seconds, and the sliding step size is 2 seconds. The standard deviation of all data within each sliding window is output, and the mean of the standard deviations of all sliding windows is calculated as the instability of the grid frequency change rate at the current data collection time of the new energy power plant grid connection point. Here, the instability represents the average fluctuation intensity of the grid frequency change rate within a continuous time window, reflecting the overall instability of the frequency of the intelligent dispatch power system and quantifying the dispersion of the frequency change rate. The larger the value, the more severe the grid frequency fluctuation, and the closer the intelligent dispatch power system is to instability risk, requiring more reserve capacity for adjustment.

[0048] Based on the above analysis, the high penetration imbalance index A at each data collection point of the new energy power station is calculated, and its calculation formula is as follows:

[0049] In the formula, A is the high-penetration imbalance index at the current data collection time of the new energy power plant grid connection point; D is the fluctuation amplitude of the output power at the current data collection time of the new energy power plant grid connection point, which captures the local mutation characteristics of the output signal on a minute scale, representing the maximum minute-level fluctuation amplitude of the new energy power output, indicating the severity of power shortage or surplus; P is the instability of the grid frequency change rate at the current data collection time of the new energy power plant grid connection point, which quantifies the dispersion of the frequency change rate within a continuous time window, reflects the statistical characteristics of frequency fluctuation, represents the overall instability of the frequency of the intelligent dispatch power system, and is directly related to the risk of power imbalance in the intelligent dispatch power system.

[0050] Among them, the high penetration imbalance index A quantifies the comprehensive dynamic adjustment demand risk of the smart dispatch power system in the context of high-penetration new energy scenarios, and describes the urgency of co-optimizing power balance and equipment lifespan of the smart dispatch power system. The larger the A value, the more the smart dispatch power system faces dual pressures at the current moment: the minute-level drastic fluctuations of new energy sources combined with high-frequency oscillations not only increase the probability of frequency exceeding limits and voltage collapse, but also force traditional units or energy storage to operate frequently, exacerbating equipment wear and tear.

[0051] Furthermore, the high permeability imbalance index A calculated at all previous collection times is sorted in ascending order of time to obtain the permeability imbalance sequence at each collection time.

[0052] Step 3: Based on the changing trends and fluctuations of all high-permeability imbalance indices before each collection time, obtain the hybrid energy storage out-of-control data at each collection time of the new energy power station grid connection point.

[0053] Due to the drastic fluctuations in the output of new energy sources and the accelerated aging caused by frequent deep charging and discharging of lithium battery energy storage, intelligent dispatch power systems face a core contradiction in high-penetration scenarios: the difficulty in coordinating the adjustment of resource lifespan loss with the real-time power balance requirements.

[0054] Therefore, in this embodiment, taking the current penetration imbalance sequence of the grid connection point of the new energy power station as an example, the penetration imbalance sequence is used as input, and the exponential weighted moving average (EWMA) algorithm is used. The smoothing factor is set to 0.2 to take into account both real-time performance and trend stability, and the sliding window is set to 10 seconds to match the minute-level adjustment demand cycle. The mean of the smoothed high penetration imbalance index is output. This output filters out high-frequency noise and reflects the continuous cumulative intensity trend of the adjustment demand of the intelligent dispatch power system. The larger the value, the more significant the long-term pressure of new energy fluctuations on the balance of the intelligent dispatch power system.

[0055] Furthermore, in this embodiment, the peak and valley values ​​of the permeation imbalance sequence are extracted. In this embodiment, the peak and valley values ​​of the permeation imbalance sequence at the current acquisition time are extracted using a peak-valley detection algorithm. The difference between the maximum peak value and the minimum valley value is used as the fluctuation coefficient G of the permeation imbalance sequence at the current acquisition time of the new energy power station grid connection point. This quantifies the extreme abrupt change in power deficit or surplus within the window. The larger the G value, the higher the risk of instantaneous instability faced by the intelligent dispatch power system, and the more urgent the need for millisecond-level rapid response resource intervention.

[0056] Based on the above analysis, the hybrid energy storage disconnection schedule B is constructed for each data collection time at the grid connection point of the new energy power station. The specific calculation formula is as follows:

[0057] In the formula, B represents the hybrid energy storage outage at the current data collection time of the new energy power station's grid connection point. is the mean of the high-penetration imbalance index after smoothing the penetration imbalance sequence of the new energy power plant grid connection point at the current collection time using the exponentially weighted moving average algorithm. It quantifies the continuous cumulative intensity trend of the regulation demand of the intelligent dispatch power system and reflects the long-term pressure of new energy fluctuations on power balance. G is the fluctuation coefficient of the penetration imbalance sequence of the new energy power plant grid connection point at the current collection time. It quantifies the extreme abrupt change amplitude of power imbalance risk and reflects the severity of instantaneous instability of the intelligent dispatch power system. It characterizes the sudden risk caused by new energy fluctuations. tanh() is the hyperbolic tangent function.

[0058] Among them, the hybrid energy storage outage dispatch in the context of high-penetration new energy scenarios comprehensively quantifies the dynamic matching degree between the power balance demand of the smart dispatch power system and the regulation capacity of hybrid energy storage resources. The larger the hybrid energy storage outage dispatch, the more the smart dispatch power system faces the dual challenges of long-term cumulative regulation pressure and short-term extreme fluctuation risk, revealing the comprehensive vulnerability of the smart dispatch power system in the context of high-penetration new energy environments.

[0059] Step 4: Use the joint risk threshold of hybrid energy storage for dispatch to carry out hierarchical coordinated control.

[0060] Due to the inherent volatility and intermittency of renewable energy output, smart dispatch power systems face the challenge of needing to adjust resources rapidly at the millisecond to minute level in high-penetration scenarios to instantly fill power deficits or absorb excess power in order to maintain real-time power balance and frequency stability. This limits the long-term operational resilience and economic efficiency of smart dispatch power systems under high-proportion renewable energy access.

[0061] Therefore, a hierarchical control strategy needs to be designed based on the power outage of hybrid energy storage to achieve optimized utilization of hybrid energy storage and proactive protection of lithium battery life, ensuring that the lifespan of critical equipment is maximized while maintaining power balance. The specific hierarchical control strategy design is as follows:

[0062] In this embodiment, preferably, a low-risk threshold is set. and high risk threshold In this embodiment, the low-risk threshold and the high-risk threshold are set to 0.4 and 0.8, respectively.

[0063] 1) When This indicates that the intelligent dispatch power system is in a low-risk and normal fluctuation state. At this time, the fluctuations in the intelligent dispatch power system are relatively mild, the intensity of adjustment demand is low, and the suddenness is small. A strategy of only calling upon supercapacitors to respond to millisecond- to second-level fluctuations is adopted. Specifically, supercapacitors have ultra-fast response speeds and extremely high cycle life, sufficient to cope with such fluctuations, while keeping lithium batteries in standby or low-power operation states to avoid unnecessary charge-discharge cycles and focus on lifespan protection.

[0064] 2) When At this time, it indicates that the intelligent dispatch power system is in a medium-risk state, with significant fluctuations, and adopts a hybrid coordinated operation strategy. Specifically, supercapacitors undertake the main task of smoothing out rapid fluctuations at the second level, while lithium batteries are responsible for handling relatively smooth power regulation needs at the minute level, mainly used to compensate for power differences or surpluses caused by slower changes in renewable energy output;

[0065] 3) When This indicates that the intelligent power dispatch system is in a high-risk and extremely volatile state, and a lithium battery extreme protection strategy is adopted. Specifically, the supercapacitor operates at full capacity, releasing its maximum power capability to cope with extreme second-level power shortages or shocks; the lithium battery's rated power output is limited, providing only the most basic minute-level power support to achieve extreme protection and avoid deep, high-rate charging and discharging when the system is under great pressure.

[0066] Preferably, in this embodiment, the supercapacitor response and lithium battery processing are as follows: The supercapacitor response system achieves rapid bidirectional power regulation at the millisecond to second level through a power electronic converter. When an instantaneous power deficit is detected, the supercapacitor triggers discharge based on high-frequency data from the PMU, releasing stored charge at an ultra-high rate (C-rate) to instantly fill the power gap. Conversely, when new energy sources surge, it immediately absorbs excess power, and its physical characteristics ensure that the capacity does not significantly decay after tens of thousands of charge-discharge cycles. The lithium battery processing adopts a graded power constraint strategy: In medium-risk scenarios, the battery management system (BMS) dynamically limits the depth of charge and discharge, responding only to minute-level smooth power demands to avoid deep cycling; in high-risk scenarios, a hard protection mechanism is activated, actively reducing the output power to below 50% of the rated value and switching to constant voltage mode to provide basic energy support.

[0067] It should be noted that the response and switching of supercapacitors and lithium batteries are well-known technologies and will not be elaborated upon here.

[0068] In this embodiment, the hierarchical strategy dynamically senses the balance risk level and regulation demand characteristics of the intelligent dispatch power system through hybrid energy storage outage dispatch, and intelligently allocates the regulation tasks of supercapacitors and lithium batteries. Under the premise of meeting the real-time power balance and frequency stability of the intelligent dispatch power system, it prioritizes the use of the most suitable resources. In response to the short lifespan of lithium batteries, it limits their operating depth under medium risk and actively reduces their operating rate and seeks backup support under high risk, thereby systematically solving the contradiction between the consumption of new energy fluctuations and the lifespan loss of regulation equipment.

[0069] Based on the same inventive concept as the above method, this application also provides a novel power system power balance related device that considers the consumption of new energy sources, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the novel power system power balance method that considers the consumption of new energy sources described above.

[0070] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0071] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0072] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. A power system power balance method considering the consumption of new energy sources, characterized in that, Includes the following steps: The grid frequency change rate and the actual power output data of each renewable energy power station at each data collection point are obtained at each data collection time, and the grid frequency change rate sequence and renewable energy power output sequence are obtained. By analyzing the fluctuation of renewable energy output data at renewable energy power plant grid connection points and combining it with the instability of grid frequency change rate data, a high penetration imbalance index is obtained at each data collection time of renewable energy power plant grid connection points. Based on the changing trends and fluctuations of all high-permeability imbalance indices before each collection time, the hybrid energy storage out-of-control situation at each collection time of the new energy power station grid connection point is obtained. Hierarchical coordinated control is achieved by utilizing the joint risk threshold of hybrid energy storage for energy loss dispatch. The method for obtaining the high penetration imbalance index at each data collection point of the new energy power station is as follows: In the formula, A is the high penetration imbalance index at the current data collection time of the new energy power plant grid connection point, D is the fluctuation amplitude of the output power at the current data collection time of the new energy power plant grid connection point, and P is the instability of the grid frequency change rate at the current data collection time of the new energy power plant grid connection point. The method for obtaining the hybrid energy storage out-of-dispatch data at each data collection time of the new energy power station grid connection point is as follows: In the formula, B represents the hybrid energy storage outage at the current data collection time of the new energy power station's grid connection point. G is the mean of the high penetration imbalance index after smoothing the penetration imbalance sequence at the current time of data collection at the grid connection point of the new energy power station using an exponentially weighted moving average algorithm. G is the fluctuation coefficient of the penetration imbalance sequence at the current time of data collection at the grid connection point of the new energy power station.

2. The power system power balance method considering renewable energy consumption as described in claim 1, characterized in that, The grid frequency change rate and actual power output data of each renewable energy power station at all previous data collection times are sorted in ascending order of time to obtain the grid frequency change rate sequence and renewable energy output sequence of each renewable energy power station at each data collection time.

3. The power system power balance method considering renewable energy consumption as described in claim 1, characterized in that, The new energy power output sequence at the current time of data collection at the grid-connected point of the new energy power plant is used as input. Wavelet decomposition algorithm is used to output the detail coefficients at each scale. The maximum absolute value of all detail coefficients under the maximum decomposition level is calculated and used as the fluctuation amplitude of the output power at the current time of data collection at the grid-connected point of the new energy power plant.

4. The power system power balance method considering renewable energy consumption as described in claim 1, characterized in that, The grid frequency change rate sequence at the current data collection time of the new energy power plant grid connection point is used as input. The standard deviation is calculated using a sliding window. The standard deviation of all data in each sliding window is output. The mean of the standard deviations of all sliding windows is used as the instability of the grid frequency change rate at the current data collection time of the new energy power plant grid connection point.

5. The power system power balance method considering renewable energy consumption as described in claim 1, characterized in that, Using the current data collection time of the penetration imbalance sequence at the grid connection point of the new energy power station as input, the mean of the smoothed high penetration imbalance index is output using the exponential weighted moving average algorithm.

6. The power system power balance method considering renewable energy consumption as described in claim 1, characterized in that, Extract the peak and valley values ​​of the penetration imbalance sequence at the current collection time of the new energy power station grid connection point, and use the difference between the maximum peak value and the minimum valley value as the fluctuation coefficient of the penetration imbalance sequence at the current collection time of the new energy power station grid connection point.

7. The power system power balance method considering renewable energy consumption as described in claim 1, characterized in that, The hierarchical coordinated control using a joint risk threshold for hybrid energy storage misalignment further includes: Preset low-risk threshold and high risk threshold ,when At that time, a strategy of responding to millisecond to second-level fluctuations in the supercapacitor response system is adopted; when When a hybrid coordination operation strategy is adopted, it is used; when At that time, a lithium battery limit protection strategy is adopted.

8. Power system balance-related equipment considering renewable energy consumption, including a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the power system power balance method considering the consumption of new energy sources as described in any one of claims 1-7.

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