Capacity compensation mechanism cooperation method adaptive to regional power supply structure

Abstract

The invention relates to the technical field of power compensation, in particular to a capacity compensation mechanism cooperation method adaptive to a regional power supply structure, and the method comprises the steps: obtaining data, and recognizing the power demand of an abnormal load; determining a new energy risk level; determining an energy storage risk level; adjusting a preset load demand quantity threshold value; determining a target compensation unit; adjusting a preset new energy power supply threshold value; and generating a capacity compensation list. According to the method, the load demand electric quantity, the new energy generating capacity, the power generation fluctuation frequency and the key parameters influencing the energy storage power supply capacity are obtained, the preset threshold value is dynamically adjusted, and the capacity compensation list is generated for reference of the power plant, so that intelligent power dispatching and power supply safety are achieved, efficient utilization of power resources and continuous and stable operation of the load end are ensured; the method effectively solves the problems of unstable power supply and lagged power dispatching in the case of load demand fluctuation and power generation fluctuation due to excessive dependence on static parameters and artificial experience adjustment.

Description

Technical Field

[0001] This invention relates to the field of power compensation technology, and in particular to a coordinated method for capacity compensation mechanisms adapted to regional power supply structures. Background Technology

[0002] With the vigorous development of regional new energy sources and the rapid growth of new energy power generation in power plants, the regional power structure is undergoing profound changes. However, new energy power generation is characterized by strong intermittency and volatility, posing a severe challenge to the stability of power supply from power plants. Simultaneously, the complex and diverse energy structures within power plants, coupled with the significant influence of meteorological conditions on new energy output, lead to uncertainties between load demand and power supply capacity, further jeopardizing the safe power supply of power plants. In this context, how to accurately identify power supply risks in real time, rationally compensate capacity, and achieve dynamic adjustments based on the characteristics of new energy power generation, energy storage status, and load demand, thereby providing power plants with scientific and reliable operational decisions, has become a critical issue that urgently needs to be addressed to ensure power supply continuity and improve energy dispatch efficiency.

[0003] Chinese Patent Application Publication No. CN113078643A discloses a method for configuring energy storage capacity for wind power planning optimization and deviation compensation considering the economic operation of the power grid. The method includes: Step 1: Statistically analyzing the daily planned and actual power generation data of wind power generation over a year, calculating the planned deviation from 0:00 to 24:00 each day, performing power-time integral statistics on the power deviation data for continuous sampling periods, and performing kernel density statistics on the daily integral results over a year to initially obtain the energy storage capacity configuration amount for planned deviation compensation; Step 2: Statistically analyzing the daily load changes of the power grid where the wind power generation is located over a year, dividing the load demand into periods of rising demand, peak demand, declining demand, low demand, and uncertain demand, and assigning different time intervals to these periods. Then, performing kernel density statistics on the daily statistical results over a year to obtain the annual distribution of load demand characteristics by time interval; Step 3: ... Step 3: Set different deviation compensation load adaptation factors for the deviations required to be compensated in different time periods with deterministic load changes, and then adjust the deviation samples to be compensated. Perform power time integral statistics. Based on the daily power time integral statistics, perform annual statistics. Then perform kernel density statistics on the daily integral statistics within the year to obtain the initial correction results of the planned deviation compensation energy storage capacity configuration. Step 4: From the perspective of the economic benefits of wind power operation in the power market, and from the perspective of peak and valley electricity prices, power generation bidding and energy storage costs, set deviation compensation relaxation factors. Based on the deviation compensation samples in Step 3, adjust the deviation samples again, perform power time integral statistics, and make a second correction to the energy storage configuration demand. Step 5: Finally, according to the daily predicted deviation compensation demand configuration, perform kernel density probability statistical analysis to select the final capacity configuration and improve the utilization efficiency of energy storage.

[0004] Therefore, the proposed method for optimizing wind power plans and compensating for deviations in consideration of grid economic operation has the following problems: This method divides load demand into fixed time periods, such as peak and off-peak periods, which cannot reflect the dynamic changes in grid load in real time, and is prone to inaccurate energy storage configuration, resulting in over- or under-compensation of capacity compensation; This method mainly relies on long-term statistics and does not consider the instantaneous fluctuations of wind power, making it difficult to cope with short-term generation deviations; This method adjusts compensation factors based on historical data, which may not respond in a timely manner and is prone to failure to quickly adjust energy storage configuration when grid load changes abruptly. Summary of the Invention

[0005] To address this, the present invention provides a capacity compensation mechanism coordination method adapted to regional power structure. This method integrates key parameters related to the load end and the power supply end of the power plant with a dynamic adjustment mechanism to overcome the problems of unstable power supply and lagging power dispatch caused by excessive reliance on static parameters and manual experience in the prior art when facing fluctuations in load demand and power generation.

[0006] To achieve the above objectives, the present invention provides a coordinated method for capacity compensation mechanisms adapted to regional power supply structures, comprising: Real-time acquisition of load demand, renewable energy generation, power generation fluctuation frequency, energy storage reserve capacity, charging and discharging rate, and historical cycle count of the load end within the power plant's power supply area. Based on the load demand power and the preset load demand threshold, determine whether a power supply abnormality has occurred to obtain a first determination result; Based on the first determination result, the risk level of new energy is determined according to the new energy power generation, the power generation fluctuation frequency and the preset new energy power supply threshold. The energy storage risk level is determined based on the new energy risk level, the energy storage backup capacity, the charge / discharge rate, and the historical cycle count. The preset load demand threshold is adjusted according to the time distribution characteristics of the new energy risk level and the energy storage risk level within the next preset adjustment period. Several target compensation units are determined based on the energy storage risk level redefined after adjusting the preset load demand threshold. The preset new energy power supply threshold is adjusted according to the location distribution characteristics of the target compensation unit within the next preset coordination time. Capacity compensation is performed based on the energy storage reserve capacity of the target compensation unit, which is re-determined after adjusting the preset new energy power supply threshold.

[0007] Furthermore, the process of determining whether a power supply abnormality has occurred based on the load demand and a preset load demand threshold to obtain a first determination result includes: When the power demand of the load exceeds the preset load demand threshold, a power supply abnormality is determined to have occurred, thus obtaining the first determination result.

[0008] Furthermore, the process of determining the risk level of new energy sources based on the new energy power generation, the power generation fluctuation frequency, and the preset new energy power supply threshold includes: The power generation of the new energy source and the power generation fluctuation frequency within the preset risk period are normalized respectively to obtain the power normalization value dataset and the frequency normalization value dataset. Based on the first determination result, the average value of the normalized power value dataset, the average value of the normalized frequency value dataset, the preset power weight, and the preset frequency weight are weighted and summed to obtain the new energy power supply index. When the new energy power supply index is less than the preset new energy power supply threshold, the new energy risk level is determined to be high-risk.

[0009] Furthermore, the process of determining the energy storage risk level based on the new energy risk level, the load demand electricity, the energy storage reserve capacity, the charge / discharge rate, and the historical cycle count includes: When the new energy risk level is the new energy high risk, the charging and discharging rate and the load demand power within the next preset level time are normalized to obtain the rate normalized dataset and the load normalized dataset. Calculate the Pearson correlation coefficient between the rate-normalized dataset and the load-normalized dataset to obtain the energy storage load correlation. When the energy storage load correlation is lower than a preset correlation threshold, the energy storage risk level is determined to be high-level energy storage risk based on the energy storage reserve capacity and the number of historical cycles.

[0010] Furthermore, the process of determining the energy storage risk level as high-risk based on the energy storage reserve capacity and the historical cycle count includes: When the number of historical cycles exceeds a preset threshold, the decay rate of the energy storage reserve capacity within the preset level duration is calculated to obtain the capacity decay rate. When the capacity decay rate exceeds a preset deceleration threshold, the energy storage risk level is determined to be high-risk.

[0011] Furthermore, the process of adjusting the preset load demand threshold based on the time distribution characteristics of the new energy risk level and the energy storage risk level within the next preset adjustment period includes: The duration of occurrence of the new energy risk level as the high risk of the new energy within the preset adjustment period is calculated to obtain the high energy risk duration. The duration of occurrence of the energy storage risk level as the high risk of the energy storage within the preset adjustment period is calculated to obtain the high storage risk duration. Calculate the ratio of the high-risk duration to the preset adjustment duration to obtain the high-risk duration percentage; When the proportion of high-energy duration is greater than a preset high-energy threshold, the ratio of the high-risk storage duration to the preset adjustment duration is calculated to obtain the proportion of high-risk storage duration. When the proportion of high storage duration is greater than the preset high storage threshold, the preset load demand threshold is reduced based on the relative deviation between the high storage duration and the preset high storage threshold.

[0012] Furthermore, the process of determining several target compensation units based on the energy storage risk level redefined after adjusting the preset load demand threshold includes: When the energy storage risk level is the high-risk energy storage level, a number of the distributed energy units corresponding to the high-risk energy storage level are identified as the target compensation units.

[0013] Furthermore, the process of adjusting the preset new energy power supply threshold based on the location distribution characteristics of the target compensation units within the next preset coordination time period includes: Obtain the straight-line distance between the target compensation unit and the preset reference center point to obtain several target distances; Calculate the reciprocal of the standard deviation of all the target distances to obtain the target concentration. The preset new energy power supply threshold is adjusted based on the target concentration and the preset concentration threshold.

[0014] Furthermore, the process of adjusting the preset new energy power supply threshold based on the target concentration and the preset concentration threshold includes: When the target concentration is less than the preset concentration threshold, the preset new energy power supply threshold is increased according to the relative deviation between the target concentration and the preset concentration threshold.

[0015] Furthermore, the process of capacity compensation based on the energy storage reserve capacity of the target compensation unit, which is re-determined after adjusting the preset new energy power supply threshold, includes: The compensation priority of the target compensation unit is determined by sorting the energy storage backup capacity from smallest to largest, and a capacity compensation list is generated according to the compensation priority.

[0016] Compared with existing technologies, the beneficial effects of this invention lie in achieving precise scheduling and flexible allocation of power from power plants by introducing a multi-level risk assessment and dynamic adjustment mechanism. By acquiring and analyzing load demand, renewable energy generation, power generation fluctuation frequency, and key parameters affecting energy storage power supply capacity, the invention automatically optimizes the adjustment of preset load demand thresholds and preset renewable energy power supply thresholds. This not only effectively suppresses the impact of the intermittency and volatility of renewable energy generation on the stability of the regional power grid, but also generates a capacity compensation list through compensation priorities, thereby achieving intelligent scheduling and stable operation of the power system. This ensures the efficient utilization of power resources and the continuous and stable operation of the load side, effectively solving the problems of unstable power supply and lagging power scheduling caused by excessive reliance on static parameters and manual experience adjustments when facing fluctuations in load demand and power generation.

[0017] Furthermore, by monitoring the load demand in real time and comparing it with a preset load demand threshold, rapid detection of power supply anomalies is achieved. When the actual load demand exceeds the preset threshold, the system can immediately identify potential power supply pressure or energy shortages, thus obtaining an initial judgment. This judgment based on real-time quantitative data eliminates reliance on experience or manual intervention for anomaly detection, reflecting the dynamic characteristics of load changes and promptly triggering subsequent risk level assessments and capacity compensation strategies. This improves the power system's response speed to load fluctuations and enhances power supply reliability, effectively reducing power outages or uneven energy distribution caused by unexpected loads.

[0018] Furthermore, by normalizing the power generation and frequency fluctuations of new energy sources, and combining this with the first judgment result for weighted calculation, the real-time status of new energy power supply capacity can be quantitatively assessed. The average values ​​of the normalized power generation dataset and the normalized frequency dataset are weighted and summed according to preset weights to obtain the new energy power supply index, enabling the system to simultaneously consider the impact of total power generation and fluctuation characteristics on power supply reliability. When the power supply index is lower than the preset new energy power supply threshold, the system determines the new energy risk level to be high risk, thereby identifying potential power shortages or fluctuation risks in advance. By quantifying and integrating multi-dimensional dynamic parameters, a scientific assessment of the instability of new energy power generation is achieved, improving overall power supply stability and responsiveness to load changes.

[0019] Furthermore, by normalizing the charging / discharging rate and load demand, the data is compared under the same standard, eliminating errors caused by differences in dimensions. By calculating the Pearson correlation coefficient between the energy storage unit's charging / discharging rate and the load demand, the responsiveness and matching degree of the energy storage system under actual load conditions can be quantified. When the calculated energy storage-load correlation is lower than a preset correlation threshold, it indicates that the energy storage system's charging / discharging behavior is not synchronized with load changes, posing a potential risk of insufficient power supply or response lag. Combining the energy storage reserve capacity and historical cycle count to further determine the energy storage risk level allows for a scientific assessment of the remaining available power and aging degree of the energy storage unit, and timely identification of high-risk situations, enabling dynamic monitoring and risk warning of the energy storage unit's operating status.

[0020] Furthermore, by combining the historical cycle count with the decay rate of energy storage reserve capacity, a dynamic assessment of the operational health status of energy storage units is achieved. Specifically, after experiencing numerous charge-discharge cycles, the capacity of an energy storage unit gradually decays, leading to a decrease in the reserve capacity available for load power supply. By setting a preset cycle count threshold, energy storage units entering the decay phase can be identified. When the cycle count exceeds this threshold, the capacity decay rate is further calculated. If the decay rate exceeds a preset deceleration threshold, it indicates a significant decrease in the capacity of the energy storage unit and a marked aging trend. At this point, it is classified as a high-level energy storage risk. This not only allows for the early detection of potential energy storage degradation issues, avoiding insufficient capacity compensation during peak load periods, but also provides a basis for dynamically adjusting dispatch strategies and energy storage maintenance plans, thereby improving the stability and security of the entire power system in long-term operation.

[0021] Furthermore, by comprehensively considering the risk levels of new energy sources and energy storage, as well as their time distribution characteristics within the preset adjustment period, the preset load demand threshold can be dynamically adjusted to ensure the stability and reliability of power supply. By statistically analyzing the duration of high-risk states for new energy and energy storage units within the preset adjustment period, the duration of these risk states can be quantified, further reflecting the current load pressure and potential risks of the power supply. By calculating the ratio of high-energy risk duration to high-storage risk duration (i.e., the proportion of high-energy duration and the proportion of high-storage duration), the relative intensity of the risk states can be objectively assessed, helping to determine whether load adjustment is necessary. When the proportion of high-energy duration exceeds the preset high-energy threshold, it indicates a significant risk for the new energy units, requiring further analysis of the energy storage unit's risk situation. If the proportion of high-storage risk duration for energy storage units also exceeds the preset high-storage threshold, the preset load demand threshold is dynamically adjusted based on the relative deviation between the high-storage duration and the preset high-storage threshold. A load adjustment coefficient is introduced to control the adjustment magnitude, thereby avoiding excessive adjustment that could lead to system instability, while ensuring the effective utilization of energy resources and load balance.

[0022] Furthermore, by redetermining the energy storage risk level based on the adjusted preset load demand threshold to determine the target compensation unit, it is possible to ensure that timely measures are taken when the energy storage system faces high risks in order to maintain the stability of power supply. When the risk level of the energy storage system is again determined to be high risk, by automatically identifying several distributed energy units where the high risk of energy storage is located as target compensation units, it is helpful to accurately dispatch relevant energy resources when there are potential risks in the energy storage system, and avoid the instability of power supply caused by the failure of a single energy source or insufficient energy storage.

[0023] Furthermore, by calculating the distance between the target compensation unit and the preset reference center point, and calculating the reciprocal of the standard deviation of the target distance, the target concentration is quantified, thereby accurately reflecting the spatial distribution characteristics of the target compensation unit. This is then compared with the preset concentration threshold, enabling dynamic adjustment of the new energy power supply threshold based on the actual distribution of the target units. This avoids energy supply imbalance caused by excessive concentration or dispersion of units within the region. The adjustment of the power supply threshold is more refined and flexible, which helps to optimize the scheduling of energy resources, improve the stability and response speed of energy supply, and ensure accurate load compensation when facing load fluctuations. It can also adapt to environmental changes in the short term, reducing resource waste and power supply risks.

[0024] Furthermore, by monitoring the target concentration and comparing it with a preset concentration threshold, when the target concentration is lower than the threshold, it indicates that the target compensation units are spatially dispersed, which may lead to uneven distribution of energy resources and insufficient power supply. By increasing the preset new energy power supply threshold, this dispersed distribution characteristic can be adapted to, and the flexibility and stability of energy supply can be effectively improved. This avoids power shortages or oversupply caused by overly dispersed target units, thereby optimizing the allocation of energy resources, improving overall power supply efficiency and response capability, and ensuring the stable operation of the system under different environments.

[0025] Furthermore, by prioritizing energy storage reserve capacities from smallest to largest, target compensation units with smaller reserve capacities can be identified first, ensuring that these units receive priority support and reducing power outages or instability caused by insufficient energy storage capacity. The capacity compensation list generated according to compensation priorities provides managers with clear operational guidance, making the capacity compensation process more efficient and orderly. This helps ensure the reliability and stability of the power grid in the event of fluctuations in renewable energy supply or changes in demand, avoiding unnecessary resource waste and enhancing the emergency response capabilities of power plants. It also enables them to better adapt to various complex changes in power demand, ensuring the continuity and stability of energy supply. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the capacity compensation mechanism coordination method adapted to the regional power structure in this embodiment; Figure 2 This is a schematic diagram illustrating the determination of the first determination result in this embodiment; Figure 3 This is a schematic diagram illustrating the determination of advanced risks in energy storage in this embodiment; Figure 4 This is a schematic diagram illustrating the adjustment of the preset new energy power supply threshold in this embodiment. Detailed Implementation

[0027] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0028] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0029] Please see Figure 1 As shown, this is a schematic diagram of the capacity compensation mechanism coordination method adapted to the regional power structure in this embodiment. This embodiment provides a capacity compensation mechanism coordination method adapted to the regional power structure, including: Real-time acquisition of load demand, renewable energy generation, power generation fluctuation frequency, energy storage reserve capacity, charging and discharging rate, and historical cycle count of the load end within the power plant's power supply area. Based on the load demand power and the preset load demand threshold, determine whether a power supply abnormality has occurred to obtain a first determination result; Based on the first determination result, the risk level of new energy is determined according to the new energy power generation, the power generation fluctuation frequency and the preset new energy power supply threshold. The energy storage risk level is determined based on the new energy risk level, the energy storage backup capacity, the charge / discharge rate, and the historical cycle count. The preset load demand threshold is adjusted according to the time distribution characteristics of the new energy risk level and the energy storage risk level within the next preset adjustment period. Several target compensation units are determined based on the energy storage risk level redefined after adjusting the preset load demand threshold. The preset new energy power supply threshold is adjusted according to the location distribution characteristics of the target compensation unit within the next preset coordination time. Capacity compensation is performed based on the energy storage reserve capacity of the target compensation unit, which is re-determined after adjusting the preset new energy power supply threshold.

[0030] In this embodiment, the capacity compensation mechanism collaborative method adapted to the regional power structure is applied to power plants. The plant area adapts to the energy structure characteristics of the location and has distributed energy units such as photovoltaic power stations, wind turbine generators and energy storage. Real-time power supply covers terminal loads such as industrial parks or urban microgrids. The load demand and the risk level of power supply of each distributed energy unit are analyzed to finally screen out the target compensation units that need to be compensated for capacity, and generate a capacity compensation list for power plant managers to compensate the target compensation units according to priority, so as to ensure efficient and safe maintenance of stable power supply.

[0031] In this embodiment, key operating parameters of power plants and distributed energy units are collected in real time to support subsequent new energy risk assessment, energy storage risk assessment, and the generation of capacity compensation lists. Distributed energy units refer to relatively independent new energy power generation or energy storage devices in power plants or power systems, such as photovoltaic power stations, wind turbines, and energy storage battery units. Each unit can independently measure and regulate its power generation or energy storage status. Load demand refers to the total electricity consumption demand of load terminals such as industrial parks or urban microgrids within a specific time period, which can be collected in real time through smart meters or power monitoring systems. New energy power generation refers to the total amount of electricity actually generated by distributed energy units per unit time. Its value is directly affected by natural environmental factors such as light intensity, wind speed, and temperature, exhibiting significant fluctuations and intermittency, and is used to assess the power supply of distributed energy units. One of the core indicators of capacity is the basic data that the system needs to refer to when formulating capacity compensation strategies. This data can be obtained through power generation monitoring devices to measure the amount of renewable energy generated; power generation fluctuation frequency reflects the degree of fluctuation in renewable energy output power over time, which can be calculated through time series analysis or spectrum analysis; energy storage reserve capacity represents the remaining electricity available to the load side under the current state of the energy storage unit, which can be monitored in real time through the energy storage management system; charge / discharge rate represents the charging or discharging power of the energy storage unit per unit time, which can be obtained through power meters or the energy storage management system; historical cycle count reflects the total number of charge / discharge cycles completed by the energy storage unit, which can be recorded and statistically analyzed through the energy storage management system. By acquiring these parameters in real time, the system can comprehensively understand the power generation and storage status, load demand changes, and risk characteristics of each distributed energy unit within the power plant, providing reliable data support for subsequent risk level determination and capacity compensation implementation.

[0032] The preset load demand threshold refers to the projected load demand set in advance in a power plant or power system based on historical data, load forecasts, or production scheduling plans. It depends on historical load trends, seasonal variations, and expected electricity demand, and is typically set between 100MW and 500MW. In this embodiment, it is set to 300MW to ensure that the system can reasonably assess power supply pressure and adjust in a timely manner under different load demand scenarios. The preset renewable energy supply threshold is the minimum renewable energy generation limit set during renewable energy supply based on the stability requirements of the power system. It depends on the generation capacity of each distributed energy unit within the power plant, system stability requirements, and the grid's acceptance capacity, and is typically set between 50MW and 150MW. In this embodiment, it is set to 100MW. This ensures that renewable energy generation maintains system stability while avoiding power shortages caused by insufficient renewable energy supply. The preset adjustment time refers to the time window used to adjust the preset load demand threshold. It depends on the system response time, the time characteristics of power load fluctuations, and the scheduling cycle. It is usually set between 10 minutes and 2 hours. In this embodiment, it is set to 30 minutes, which can quickly respond to load changes and effectively make dynamic adjustments. The preset coordination time is the time window used to analyze and adjust the preset renewable energy supply threshold. It depends on the complexity of system coordination adjustment and the response time of the target compensation unit. It is usually set between 15 minutes and 1 hour. In this embodiment, it is set to 45 minutes, which can provide sufficient data analysis period for the dynamic optimization of renewable energy supply threshold.

[0033] In this embodiment, the capacity compensation list is used to determine the compensation priority of each target compensation unit according to its energy storage reserve capacity in ascending order. Specifically, the capacity compensation list is generated based on the determination of energy storage risk level. By assessing the energy storage reserve capacity of each distributed energy unit, it ultimately determines which units need to be compensated first to ensure the stable operation of the power grid. Managers can perform capacity compensation one by one according to the priority order in the list, thereby achieving optimized power supply scheduling and ensuring timely and effective capacity replenishment when new energy supply is insufficient or when energy storage systems encounter problems, maximizing the continuity and reliability of power supply. The capacity compensation list also provides managers with a clear basis for compensation strategies, helping them make scientific and reasonable decisions when facing complex power dispatch and compensation needs, thereby effectively improving power dispatch efficiency and reducing system risks.

[0034] By introducing a multi-level risk assessment and dynamic adjustment mechanism, precise scheduling and flexible allocation of power from power plants are achieved. By acquiring and analyzing key parameters such as load demand, renewable energy generation, power generation fluctuation frequency, and the impact of energy storage power supply capacity, the system automatically optimizes the adjustment of preset load demand thresholds and preset renewable energy power supply thresholds. This not only effectively suppresses the impact of the intermittency and volatility of renewable energy generation on the stability of the regional power grid, but also generates a capacity compensation list through compensation priorities. This enables intelligent scheduling and stable operation of the power system, ensuring efficient utilization of power resources and continuous stable operation at the load end. It effectively solves the problems of unstable power supply and lagging power dispatch caused by excessive reliance on static parameters and manual experience adjustments when facing fluctuations in load demand and power generation.

[0035] Please see Figure 2 As shown, this is a schematic diagram illustrating the determination of the first determination result in this embodiment. In this embodiment, the process of determining whether a power supply abnormality has occurred based on the load demand power and a preset load demand threshold to obtain the first determination result includes: When the power demand of the load exceeds the preset load demand threshold, a power supply abnormality is determined to have occurred, thus obtaining the first determination result.

[0036] By monitoring load demand in real time and comparing it with preset load demand thresholds, the system enables rapid detection of power supply anomalies. When actual load demand exceeds the preset threshold, the system can immediately identify potential power supply pressure or energy shortages, thus obtaining an initial judgment. This real-time quantitative data-based judgment eliminates reliance on experience or manual intervention for anomaly detection, reflecting the dynamic characteristics of load changes and promptly triggering subsequent risk level assessments and capacity compensation strategies. This improves the power system's response speed to load fluctuations and enhances power supply reliability, effectively reducing power outages or uneven energy distribution caused by unexpected loads.

[0037] Specifically, the process of determining the risk level of new energy sources based on the new energy power generation, the power generation fluctuation frequency, and the preset new energy power supply threshold includes: The power generation of the new energy source and the power generation fluctuation frequency within the preset risk period are normalized respectively to obtain the power normalization value dataset and the frequency normalization value dataset. Based on the first determination result, the average value of the normalized power value dataset, the average value of the normalized frequency value dataset, the preset power weight, and the preset frequency weight are weighted and summed to obtain the new energy power supply index. When the new energy power supply index is less than the preset new energy power supply threshold, the new energy risk level is determined to be high-risk.

[0038] The preset risk duration is the length of the time window used to assess the power generation and stability of new energy sources. It depends on the cycle of new energy output changes, the frequency of weather changes, and the grid load response speed, and is typically set between 10 and 60 minutes. In this embodiment, it is set to 30 minutes, which, while balancing real-time calculation and data stability, fully reflects the comprehensive characteristics of new energy power generation and power fluctuation frequency within this time period. This ensures that the new energy risk level assessment results are both representative and can quickly respond to the occurrence of power generation anomalies. The preset power weight is a parameter used to measure the degree of influence of new energy power generation in the new energy risk level assessment. It depends on the output stability of each distributed energy unit within the power plant and the sensitivity of load demand to changes in power generation. It is typically set between 0.6 and 0.8, and in this embodiment, it is set to 0.7. This allows the risk calculation process to characterize the impact of power generation changes on the overall power supply capacity, making the risk assessment results more reflective of new energy sources. The actual fluctuation of the power supply source; the preset frequency weight is a parameter used to measure the impact of the frequency fluctuation of new energy power generation on the risk level assessment of new energy. It depends on the system's tolerance to changes in power generation frequency and the degree of impact of frequency fluctuation on grid stability. It is usually set between 0.2 and 0.4. In this embodiment, it is set to 0.3, which can reflect the impact of frequency instability on the continuity of system power supply in the weighted calculation, thereby achieving a more comprehensive judgment on the risk of new energy power generation; the new energy advanced risk refers to the calculation of the new energy power supply index by comprehensively considering the new energy power generation and the power generation fluctuation frequency in the new energy power generation system, and determining the preset power weight and preset frequency weight based on the impact of the two on the new energy power supply capacity. When the new energy power supply index is lower than the preset new energy power supply threshold, it is determined that the power generation capacity and stability of new energy are significantly insufficient within a certain period of time, which may lead to insufficient power supply or failure to meet load demand, and this is defined as the new energy advanced risk.

[0039] In this embodiment, the normalization of the new energy power generation and the power generation fluctuation frequency within the preset risk period adopts maximum-minimum value normalization, which can map the new energy power generation and the power generation fluctuation frequency within the preset risk period to a unified interval of [0,1], thereby eliminating the influence of differences in different dimensions and magnitudes on the risk calculation results. This ensures that the power generation and frequency data are comparable when weighted and summed, avoids the bias of a single high-amplitude indicator on the new energy power supply index, and makes the judgment result of the new energy risk level more stable, objective and adaptable.

[0040] By normalizing the power generation and frequency fluctuations of new energy sources and combining them with the first judgment result for weighted calculation, the real-time status of new energy power supply capacity can be quantitatively assessed. The average values ​​of the normalized power generation dataset and the normalized frequency dataset are weighted and summed according to preset weights to obtain the new energy power supply index, enabling the system to simultaneously consider the impact of total power generation and fluctuation characteristics on power supply reliability. When the power supply index is lower than the preset new energy power supply threshold, the system determines the new energy risk level to be high risk, thereby identifying potential power shortages or fluctuation risks in advance. By quantifying and integrating multi-dimensional dynamic parameters, a scientific assessment of the instability of new energy power generation is achieved, improving overall power supply stability and responsiveness to load changes.

[0041] Specifically, the process of determining the energy storage risk level based on the new energy risk level, the load demand, the energy storage reserve capacity, the charge / discharge rate, and the historical cycle count includes: When the new energy risk level is the new energy high risk, the charging and discharging rate and the load demand power within the next preset level time are normalized to obtain the rate normalized dataset and the load normalized dataset. Calculate the Pearson correlation coefficient between the rate-normalized dataset and the load-normalized dataset to obtain the energy storage load correlation. When the energy storage load correlation is lower than a preset correlation threshold, the energy storage risk level is determined to be high-level energy storage risk based on the energy storage reserve capacity and the number of historical cycles.

[0042] The preset level duration is a statistical duration used to evaluate the performance change trend of an energy storage unit over a period of time. It depends on the system load fluctuation cycle, energy storage response speed, and sampling frequency, and is typically set between 2 and 12 hours. In this embodiment, it is set to 6 hours, which can fully reflect the operational stability and capacity change patterns of the energy storage unit under load fluctuations during its daily operation cycle, providing a reliable time scale for risk level assessment. The preset correlation threshold is a standard value used to determine whether the energy storage load correlation is within the risk range. It depends on the design requirements of the energy storage system and the normal fluctuation range between load demand and charge / discharge rate, and is typically set between 0.3 and 0.7. In this embodiment, it is set to 0.5, which can effectively distinguish between the normal operating state and the potential risk state of the energy storage system, ensuring that the correlation between load demand and charge / discharge rate is low. In such cases, timely assessment and handling of energy storage risk levels are crucial. High-level energy storage risk indicates a state level where the energy storage unit already faces significant performance degradation or insufficient power supply capacity within the current operating cycle. This state is determined by comprehensively considering the energy storage unit's reserve capacity, the matching degree between its charge / discharge rate and load demand, and the historical cycle count. When the energy storage load correlation is low, it indicates that the energy storage unit's responsiveness to load changes is weakened, making timely energy compensation impossible. Simultaneously, if the energy storage reserve capacity is insufficient and the historical cycle count is high, it indicates significant battery aging and capacity degradation. In this case, classifying the risk level as high-level energy storage risk allows for early identification of potential failure risks in the energy storage system, preventing insufficient energy supply during peak load periods. This provides an early warning basis for system capacity compensation and scheduling optimization, ensuring the overall grid operation safety and power supply continuity.

[0043] In this embodiment, the normalization of the charging and discharging rate and the load demand within the next preset level time period adopts maximum-minimum value normalization. This normalization can convert data with different dimensions and different value ranges, such as charging and discharging rate and load demand, into a unified numerical range [0,1]. This eliminates the influence of dimensional differences on the calculation of energy storage load correlation, improves calculation accuracy and stability, ensures data comparison under the same standard, and provides reliable numerical support for subsequent determination of energy storage risk level.

[0044] By normalizing the charge / discharge rates and load demand, the data is compared under the same standard, eliminating errors caused by differences in dimensions. Calculating the Pearson correlation coefficient between the energy storage unit's charge / discharge rate and the load demand allows for the quantification of the energy storage system's response capability and matching degree under actual load conditions. When the calculated energy storage-load correlation is lower than a preset correlation threshold, it indicates that the energy storage system's charge / discharge behavior is not synchronized with load changes, posing a potential risk of insufficient power supply or response lag. Further determining the energy storage risk level by combining the energy storage reserve capacity and historical cycle count allows for a scientific assessment of the remaining available power and aging degree of the energy storage units, and timely identification of high-risk situations, enabling dynamic monitoring and risk warning of the energy storage unit's operating status.

[0045] Please see Figure 3 As shown, this is a schematic diagram of determining advanced energy storage risk in this embodiment. In this embodiment, the process of determining the energy storage risk level as advanced based on the energy storage reserve capacity and the historical cycle count includes: When the number of historical cycles exceeds a preset threshold, the decay rate of the energy storage reserve capacity within the preset level duration is calculated to obtain the capacity decay rate. When the capacity decay rate exceeds a preset deceleration threshold, the energy storage risk level is determined to be high-risk.

[0046] The preset cycle threshold is used to determine whether the energy storage system has reached a certain number of cycles. It depends on the lifespan of the energy storage device and the impact of the cycle count, and is usually set between 1,000 and 5,000 cycles. In this embodiment, it is set to 3,000 cycles. This can trigger the capacity decay assessment after the energy storage device has experienced a certain usage period, ensuring that the energy storage risk assessment is accurate and reasonable, and avoiding premature or delayed assessment of energy storage risks. The preset deceleration threshold is used to determine whether the energy storage reserve capacity decay rate exceeds the safety limit. It depends on the capacity retention rate of the battery material, the temperature environment, and the depth of charge and discharge. It is usually set between 2% / h and 10% / h. In this embodiment, it is set to 5% / h, which can accurately distinguish between normal fluctuations and abnormally rapid decay states of energy storage capacity, thereby triggering risk judgment and compensation strategies in advance when the capacity decline trend is obvious.

[0047] By combining the historical cycle count with the decay rate of energy storage reserve capacity, a dynamic assessment of the operational health status of energy storage units is achieved. Specifically, after experiencing numerous charge-discharge cycles, the capacity of an energy storage unit gradually decays, leading to a decrease in the reserve capacity available for power supply to the load. By setting a preset cycle count threshold, energy storage units entering the decay phase can be identified. When the cycle count exceeds this threshold, the capacity decay rate is further calculated. If the decay rate exceeds a preset deceleration threshold, it indicates a significant decrease in the capacity of the energy storage unit and a marked aging trend. At this point, it is classified as a high-level risk in energy storage. This approach not only allows for the early detection of potential energy storage degradation issues, preventing insufficient capacity compensation during peak load periods, but also provides a basis for dynamically adjusting dispatch strategies and energy storage maintenance plans, thereby improving the stability and security of the entire power system in long-term operation.

[0048] Specifically, the process of adjusting the preset load demand threshold based on the time distribution characteristics of the new energy risk level and the energy storage risk level within the next preset adjustment period includes: The duration of occurrence of the new energy risk level as the high risk of the new energy within the preset adjustment period is calculated to obtain the high energy risk duration. The duration of occurrence of the energy storage risk level as the high risk of the energy storage within the preset adjustment period is calculated to obtain the high storage risk duration. Calculate the ratio of the high-risk duration to the preset adjustment duration to obtain the high-risk duration percentage; When the proportion of high-energy duration is greater than a preset high-energy threshold, the ratio of the high-risk storage duration to the preset adjustment duration is calculated to obtain the proportion of high-risk storage duration. When the proportion of high storage duration is greater than the preset high storage threshold, the preset load demand threshold is reduced according to the relative deviation between the high storage duration and the preset high storage threshold, U'=U×[1-s×(Y-Y0) / Y0], where U is the preset load demand threshold before adjustment, U' is the preset load demand threshold after adjustment, Y is the high storage duration, Y0 is the preset high storage threshold, and s is the preset load adjustment coefficient.

[0049] The preset adjustment duration refers to the time period for adjusting the preset load demand threshold based on the time distribution characteristics of the risk levels of new energy and energy storage. It depends on the dynamic response requirements of the power system and is typically set between 1 hour and 24 hours; in this embodiment, it is set to 6 hours. This allows for timely adjustment of load demand based on real-time risk changes in new energy and energy storage, ensuring effective mitigation of grid load when risks occur and preventing power supply fluctuations due to excessive risk accumulation. The preset high-energy threshold refers to the threshold at which further judgment is needed regarding load demand adjustment when the proportion of new energy risk exceeds this threshold, taking into account the duration of energy storage risk. This depends on the stability of the new energy system and the grid's tolerance for new energy fluctuations, and is typically set between 10% and 25%; in this embodiment, it is set to 15%. This ensures timely dynamic adjustment of load demand when new energy supply is unstable or the risk is high, based on the risk status of the new energy system, preventing... To maintain the stability and reliability of the power grid, the following measures are taken: A preset high storage threshold is defined as the percentage of time the energy storage risk exceeds a certain threshold, indicating a higher risk to the energy storage system and necessitating load demand adjustment. This threshold depends on the health status of the energy storage devices and the system's fault tolerance, and is typically set between 10% and 30%. In this embodiment, it is set to 20%. This ensures stable grid operation by avoiding over-reliance on the energy storage system to compensate for load demand when it malfunctions or faces high risks. A preset load adjustment coefficient is used to control the load adjustment magnitude when adjusting the preset load demand threshold. This coefficient depends on the system's tolerance to energy storage risks and its sensitivity to load adjustments, and is typically set between 0.05 and 0.2. In this embodiment, it is set to 0.1. This allows for reasonable reduction of load demand when energy storage risks are high, preventing system overload and ensuring system stability during adjustment.

[0050] By comprehensively considering the risk levels of new energy sources and energy storage, as well as their temporal distribution characteristics within a preset adjustment period, the preset load demand threshold can be dynamically adjusted to ensure the stability and reliability of power supply. By statistically analyzing the duration of high-risk states for both new energy and energy storage units within the preset adjustment period, the duration of these risk states can be quantified, further reflecting the current load pressure and potential risks of the power supply. Calculating the ratio of high-energy risk duration to high-storage risk duration (i.e., the proportion of high-energy duration and the proportion of high-storage duration) allows for an objective assessment of the relative intensity of the risk states, helping to determine whether load adjustment is necessary. When the proportion of high-energy duration exceeds the preset high-energy threshold, it indicates a significant risk for the new energy units, requiring further analysis of the energy storage unit's risk situation. If the proportion of high-storage risk duration for energy storage units also exceeds the preset high-storage threshold, the preset load demand threshold is dynamically adjusted based on the relative deviation between the high-storage duration and the preset high-storage threshold. A load adjustment coefficient is introduced to control the adjustment magnitude, thereby avoiding over-adjustment that could lead to system instability, while ensuring the effective utilization of energy resources and load balance.

[0051] Specifically, the process of determining several target compensation units based on the energy storage risk level redefined after adjusting the preset load demand threshold includes: When the energy storage risk level is the high-risk energy storage level, a number of the distributed energy units corresponding to the high-risk energy storage level are identified as the target compensation units.

[0052] By redetermining the energy storage risk level based on the adjusted preset load demand threshold, the target compensation unit can be determined. This ensures that timely measures are taken to maintain power supply stability when the energy storage system faces high risks. When the energy storage system's risk level is again determined to be high risk, the automatic identification of several distributed energy units where the high risk of energy storage is located as target compensation units helps to accurately dispatch relevant energy resources when there are potential risks in the energy storage system, avoiding instability in power supply due to failure of a single energy source or insufficient energy storage.

[0053] Specifically, the process of adjusting the preset new energy power supply threshold based on the location distribution characteristics of the target compensation units within the next preset coordination time includes: Obtain the straight-line distance between the target compensation unit and the preset reference center point to obtain several target distances; Calculate the reciprocal of the standard deviation of all the target distances to obtain the target concentration. The preset new energy power supply threshold is adjusted based on the target concentration and the preset concentration threshold.

[0054] The preset reference center point refers to a benchmark location pre-set within the power plant to measure the distribution of target compensation units. This center point is usually selected as the core location of the power plant or a representative reference point. By calculating the distance between the target compensation unit and this center point, the spatial distribution of compensation units within the power plant can be understood, providing a basis for subsequent adjustments to the renewable energy power supply threshold. The preset coordination duration is a time window for measuring the distribution characteristics of target compensation units and adjusting the preset renewable energy threshold based on these characteristics. It depends on the periodicity of load demand fluctuations and response time requirements in the energy system, and is usually set between 15 minutes and 1 hour. In this embodiment, it is set to 30 minutes, which ensures that the distribution characteristics of target compensation units can be assessed in a short time and the renewable energy power supply threshold can be adjusted to optimize energy dispatch and compensation strategies. The preset concentration threshold is a standard value set in the target compensation unit location distribution analysis to judge the concentration degree of the target. It depends on the spatial distribution of target compensation units in the system and the concentration requirements for energy supply, and is usually set between 0.1 and 1. In this embodiment, it is set to 0.5, which can effectively balance the power supply demand of compensation units in different locations and ensure the stable operation of the overall system.

[0055] By calculating the distance between the target compensation unit and the preset reference center point, and calculating the reciprocal of the standard deviation of the target distance, the target concentration is quantified, thereby accurately reflecting the spatial distribution characteristics of the target compensation unit. This is compared with the preset concentration threshold, and the new energy power supply threshold can be dynamically adjusted according to the actual distribution of the target units. This avoids the problem of energy supply imbalance caused by excessive concentration or dispersion of units in the area. The adjustment of the power supply threshold is more refined and flexible, which helps to optimize the scheduling of energy resources, improve the stability and response speed of energy supply, and ensure accurate load compensation when facing load fluctuations. It can also adapt to environmental changes in the short term, reducing resource waste and power supply risks.

[0056] Please see Figure 4 As shown, this is a schematic diagram of adjusting the preset new energy power supply threshold in this embodiment. In this embodiment, the process of adjusting the preset new energy power supply threshold according to the target concentration and the preset concentration threshold includes: When the target concentration is less than the preset concentration threshold, the preset new energy power supply threshold is increased according to the relative deviation between the target concentration and the preset concentration threshold, N'=N×(1+t×︱X-X0︱ / X0), where N is the preset new energy power supply threshold before adjustment, N' is the preset new energy power supply threshold after adjustment, X is the target concentration, X0 is the preset concentration threshold, and t is the preset power supply adjustment coefficient.

[0057] The preset power supply adjustment coefficient is a sensitivity parameter used to adjust the new energy power supply threshold. It depends on the relationship between the target concentration of the target compensation unit and the preset concentration threshold. It is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.2, which can achieve fine adjustment and ensure that the change of the new energy power supply threshold will not be over-adjusted due to the slight change of the unit position, thereby ensuring the stability of the power supply.

[0058] By monitoring the target concentration and comparing it with a preset concentration threshold, when the target concentration is lower than the threshold, it indicates that the target compensation units are spatially dispersed, which may lead to uneven distribution of energy resources and insufficient power supply. By increasing the preset new energy power supply threshold, this can be adapted to this dispersed distribution characteristic and effectively improve the flexibility and stability of energy supply. This avoids power shortages or oversupply caused by overly dispersed target units, thereby optimizing the allocation of energy resources, improving overall power supply efficiency and response capability, and ensuring the stable operation of the system in different environments.

[0059] Specifically, the process of capacity compensation based on the energy storage reserve capacity of the target compensation unit, which is re-determined after adjusting the preset new energy power supply threshold, includes: The compensation priority of the target compensation unit is determined by sorting the energy storage backup capacity from smallest to largest, and a capacity compensation list is generated according to the compensation priority.

[0060] By prioritizing energy storage reserve capacities from smallest to largest, target compensation units with smaller reserve capacities can be identified first, ensuring that these units receive priority support and reducing power outages or instability caused by insufficient energy storage capacity. The capacity compensation list generated according to compensation priorities provides managers with clear operational guidance, making the capacity compensation process more efficient and orderly. This helps ensure the reliability and stability of the power grid in the event of fluctuations in renewable energy supply or changes in demand, avoiding unnecessary resource waste and improving the emergency response capabilities of power plants. It also enables them to better adapt to various complex changes in power demand, ensuring the continuity and stability of energy supply.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for coordinating capacity compensation mechanisms of regional power structures, characterized in that, The method comprises the following steps: obtaining, in real time, load demand power of a load end in a power supply area of a power plant, new energy power generation of each distributed energy unit in the power plant, power generation fluctuation frequency, energy storage reserve capacity, charging and discharging rate, and historical cycle times; determining whether a power supply anomaly occurs according to the load demand power and a preset load demand threshold to obtain a first determination result; determining a new energy risk level according to the new energy power generation, the power generation fluctuation frequency, and a preset new energy power supply threshold based on the first determination result; determining an energy storage risk level according to the new energy risk level, the energy storage reserve capacity, the charging and discharging rate, and the historical cycle times; adjusting the preset load demand threshold according to time distribution characteristics of the new energy risk level and the energy storage risk level in a next preset adjustment period; determining a plurality of target compensation units according to the energy storage risk level re-determined after the preset load demand threshold is adjusted; adjusting the preset new energy power supply threshold according to position distribution characteristics of the target compensation units in a next preset coordination period; performing capacity compensation on the energy storage reserve capacity of the target compensation units re-determined after the preset new energy power supply threshold is adjusted.

2. The method of claim 1, wherein the capacity compensation mechanism is adapted to the regional power structure. The process of determining whether a power supply anomaly occurs according to the load demand power and a preset load demand threshold to obtain a first determination result comprises: determining that a power supply anomaly occurs when the load demand power is greater than the preset load demand threshold to obtain the first determination result.

3. The method of claim 2, wherein the capacity compensation mechanism is adapted to the regional power structure. The process of determining a new energy risk level according to the new energy power generation, the power generation fluctuation frequency, and a preset new energy power supply threshold comprises: performing normalization processing on the new energy power generation and the power generation fluctuation frequency in a preset risk period respectively to obtain an electric quantity normalized value data set and a frequency normalized value data set; based on the first determination result, performing weighted summation calculation on an average value of the electric quantity normalized value data set, an average value of the frequency normalized value data set, a preset electric quantity weight, and a preset frequency weight to obtain a new energy power supply index; determining that the new energy risk level is a new energy high-level risk when the new energy power supply index is less than the preset new energy power supply threshold.

4. The method of claim 3, wherein the capacity compensation mechanism is adapted to the regional power structure. The process of determining an energy storage risk level according to the new energy risk level, the load demand power, the energy storage reserve capacity, the charging and discharging rate, and the historical cycle times comprises: when the new energy risk level is the new energy high-level risk, performing normalization processing on the charging and discharging rate and the load demand power in a next preset level period respectively to obtain a rate normalized data set and a load normalized data set; calculating a Pearson correlation coefficient of the rate normalized data set and the load normalized data set to obtain an energy storage load correlation degree; determining that the energy storage risk level is an energy storage high-level risk according to the energy storage reserve capacity and the historical cycle times when the energy storage load correlation degree is lower than a preset correlation threshold.

5. The method of claim 4, wherein the capacity compensation mechanism is adapted to the regional power structure. The process of determining that the energy storage risk level is an energy storage high-level risk according to the energy storage reserve capacity and the historical cycle times comprises: When the historical cycle number is greater than a preset number threshold, a decay rate of the energy storage reserve capacity in the preset level duration is calculated to obtain a capacity decay rate; When the capacity decay rate is greater than a preset decay threshold, it is determined that the energy storage risk level is a high-level energy storage risk.

6. The method of claim 5, wherein the capacity compensation mechanism is adapted to the regional power structure. The process of adjusting the preset load demand quantity threshold according to the time distribution characteristics of the new energy risk level and the energy storage risk level in the next preset adjustment duration includes: The occurrence duration of the new energy risk level being the new energy high-level risk in the preset adjustment duration is counted to obtain a high-energy risk duration, and the occurrence duration of the energy storage risk level being the energy storage high-level risk in the preset adjustment duration is counted to obtain a high-energy storage risk duration; The ratio of the high-energy risk duration to the preset adjustment duration is calculated to obtain a high-energy duration proportion; When the high-energy duration proportion is greater than a preset high-energy threshold, the ratio of the high-energy storage risk duration to the preset adjustment duration is calculated to obtain a high-energy storage duration proportion; When the high-energy storage duration proportion is greater than a preset high-energy storage threshold, the preset load demand quantity threshold is reduced according to the relative deviation of the high-energy storage duration and the preset high-energy storage threshold.

7. The method of claim 6, wherein the capacity compensation mechanism is adapted to the regional power structure. The process of determining a plurality of target compensation units according to the energy storage risk level re-determined after adjusting the preset load demand quantity threshold includes: When the energy storage risk level is the energy storage high-level risk, a plurality of distributed energy units corresponding to the energy storage high-level risk are determined as the target compensation units.

8. The method of claim 7, wherein the capacity compensation mechanism is adapted to the regional power structure. The process of adjusting the preset new energy power supply threshold according to the position distribution characteristics of the target compensation units in the next preset coordination duration includes: The straight-line distance between the target compensation units and a preset reference center point is obtained to obtain a plurality of target distances; The reciprocal of the standard deviation of all target distances is calculated to obtain a target concentration degree; The preset new energy power supply threshold is adjusted according to the target concentration degree and a preset concentration degree threshold.

9. The method of claim 8, wherein the capacity compensation mechanism is adapted to the regional power structure. The process of adjusting the preset new energy power supply threshold according to the target concentration degree and a preset concentration degree threshold includes: When the target concentration degree is less than the preset concentration degree threshold, the preset new energy power supply threshold is increased according to the relative deviation of the target concentration degree and the preset concentration degree threshold.

10. The method of claim 9, wherein the capacity compensation mechanism is adapted to the regional power structure. The process of capacity compensation according to the energy storage reserve capacity of the target compensation units re-determined after adjusting the preset new energy power supply threshold includes: The energy storage reserve capacity is sorted from small to large to determine the compensation priority of the target compensation units, and a capacity compensation list is generated according to the compensation priority.

Images