Resource scheduling method and system for integrated power supply system

Abstract

The invention relates to the technical field of integrated power supply system resource scheduling, in particular to an integrated power supply system resource scheduling method and system, and the method comprises the following steps: obtaining a quantified power grid access risk index, an energy storage unit health risk index and a standby power supply response capability risk index; performing importance priority level-to-level management on various loads in the service area of the integrated power supply system to obtain an average priority weight of each load; calculating a comprehensive risk value; comparing the comprehensive risk value with a preset comprehensive risk threshold value, and triggering an emergency rescheduling mode when the comprehensive risk value exceeds the comprehensive risk threshold value; dynamically adjusting various resource scheduling strategies of the integrated power supply system; and iteratively adjusting the resource scheduling strategy until the comprehensive risk value falls below the comprehensive risk threshold. The resource scheduling strategy can be responded and adjusted in time, and economy, equipment health and power supply reliability are effectively balanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated power supply system resource scheduling, and particularly relates to an integrated power supply system resource scheduling method and system. BACKGROUND

[0002] An integrated power supply system is usually deployed in scenarios such as industrial parks, commercial complexes or smart parks, and its core function is to comprehensively manage various energy resources such as photovoltaic power generation units, battery energy storage units, power access points and backup generators. The basic goal of the system is to develop an optimal resource scheduling scheme through its scheduling decision function based on predicted data of future power generation, power load and time-of-use electricity prices. This scheme aims to maximize the reduction of system operating costs while ensuring the stability of power supply. However, the actual operating environment is often full of variables and may not always evolve according to the preset prediction model. For example, if a large community event is temporarily held in the service area of the system, the power demand of such events is often difficult to be fully captured by the "24-hour load power prediction program", resulting in a significant increase in actual power load in a specific period, which exceeds the conventional prediction range, making the original scheduling plan based on prediction invalid, and thus affecting the reliability of power supply. SUMMARY

[0003] The present application aims to address the above-mentioned deficiencies by providing an integrated power supply system resource scheduling method and system.

[0004] The present application adopts the following technical solutions: An integrated power supply system resource scheduling method, the method comprising the following steps: Real-time sensing of the power grid access risk, energy storage unit health risk and backup power response capability risk faced by the integrated power supply system during operation, and quantifying the power grid access risk, energy storage unit health risk and backup power response capability risk to obtain the quantified power grid access risk index, energy storage unit health risk index and backup power response capability risk index; Importance priority classification management of various loads in the service area of the integrated power supply system to obtain the average priority weight of various loads; Based on the quantified power grid access risk index, energy storage unit health risk index, backup power response capability risk index and average priority weight of various loads, a comprehensive risk value is calculated; Comparing the comprehensive risk value with a preset comprehensive risk threshold, and triggering an emergency rescheduling mode when the comprehensive risk value exceeds the comprehensive risk threshold; In emergency rescheduling mode, the resource scheduling strategies of the integrated power system are dynamically adjusted based on the risk type that contributes the most among the grid access risk index, energy storage unit health risk index and backup power response capability risk index. After dynamically adjusting various resource scheduling strategies of the integrated power system, the operating status of the integrated power system is continuously monitored, and the resource scheduling strategy is iteratively adjusted according to changes in the operating status until the comprehensive risk value falls below the comprehensive risk threshold.

[0005] This technical solution enables real-time and comprehensive assessment of various risks faced by integrated power systems. Based on the risk situation and load importance, it dynamically triggers emergency rescheduling, thereby responding promptly and adjusting resource scheduling strategies when the system faces challenges, effectively balancing economy, equipment health, and power supply reliability, and preventing the system from falling into difficulties.

[0006] This application also discloses an integrated power system resource scheduling system, applied to an integrated power system resource scheduling method, the system comprising: The quantification module senses in real time the grid access risk, energy storage unit health risk and backup power response capability risk faced by the integrated power system during operation, and quantifies the grid access risk, energy storage unit health risk and backup power response capability risk to obtain quantified grid access risk index, energy storage unit health risk index and backup power response capability risk index. The management module performs importance priority classification management of various loads within the service area of ​​the integrated power system, and obtains the average priority weight of each type of load; The processing module calculates a comprehensive risk value based on the quantified grid access risk index, energy storage unit health risk index, backup power response capability risk index, and average priority weight of various loads. It compares the comprehensive risk value with a preset comprehensive risk threshold and triggers an emergency rescheduling mode when the comprehensive risk value exceeds the comprehensive risk threshold. The adjustment module, in emergency rescheduling mode, dynamically adjusts the various resource scheduling strategies of the integrated power system based on the risk type that contributes the most among the grid access risk index, energy storage unit health risk index, and backup power response capability risk index. The iterative module continuously monitors the operating status of the integrated power system after dynamically adjusting various resource scheduling strategies of the integrated power system, and iteratively adjusts the resource scheduling strategies according to changes in the operating status until the comprehensive risk value falls below the comprehensive risk threshold.

[0007] Through modular design, functions such as risk perception, load management, risk assessment, strategy adjustment, and iterative optimization are clearly defined, thereby providing hardware and software support for the intelligent scheduling of integrated power systems and ensuring the effective implementation of the method.

[0008] By introducing a multi-dimensional risk quantification and dynamic adjustment mechanism, this application can avoid scheduling dilemmas caused by the failure of a single prediction model, effectively protect the health of energy storage batteries, reduce high electricity purchase costs and punitive fees, and ensure the reliability of power supply to end users. Thus, under complex and real-time changing operating conditions, it can quickly and effectively generate a rescheduling scheme that can take all objectives into account and achieve the optimal compromise, significantly improving the operational resilience and economic benefits of the integrated power system.

[0009] To further understand the features and technical content of the present invention, please refer to the following detailed description and accompanying drawings. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0010] Figure 1 This is a flowchart of the integrated power system resource scheduling method of the present invention; Figure 2 This is a schematic diagram of the integrated power system resource scheduling system of the present invention. Detailed Implementation

[0011] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0012] This embodiment provides an integrated power system resource scheduling method and system, combined with Figure 1 and Figure 2 As shown.

[0013] refer to Figure 1 An integrated power system resource scheduling method, the method comprising the following steps: The system can perceive in real time the grid access risk, energy storage unit health risk and backup power response capability risk faced by the integrated power system during operation, and quantify the grid access risk, energy storage unit health risk and backup power response capability risk to obtain quantified grid access risk index, energy storage unit health risk index and backup power response capability risk index. The importance priority of various loads within the service area of ​​the integrated power system is classified and managed to obtain the average priority weight of each type of load. The comprehensive risk value is calculated based on the quantified grid access risk index, energy storage unit health risk index, backup power response capability risk index, and average priority weight of various loads. The comprehensive risk value is compared with a preset comprehensive risk threshold, and an emergency rescheduling mode is triggered when the comprehensive risk value exceeds the comprehensive risk threshold. In emergency rescheduling mode, the resource scheduling strategies of the integrated power system are dynamically adjusted based on the risk type that contributes the most among the grid access risk index, energy storage unit health risk index and backup power response capability risk index. After dynamically adjusting various resource scheduling strategies of the integrated power system, the operating status of the integrated power system is continuously monitored, and the resource scheduling strategy is iteratively adjusted according to changes in the operating status until the comprehensive risk value falls below the comprehensive risk threshold.

[0014] "Integrated power system" typically refers to a microgrid or distributed energy system that integrates multiple energy forms (such as photovoltaics, energy storage, grid power, and backup generators). Its core lies in achieving optimal energy utilization through intelligent scheduling. "Grid connection risk" refers to the risks that an integrated power system may face when connected to an external power grid, such as instantaneous power fluctuations, voltage instability, or excessively high electricity purchase costs. "Energy storage unit health risk" mainly focuses on the operating status of energy storage devices (such as batteries), such as the risks that overcharging, over-discharging, and overheating may shorten the lifespan of the equipment or cause damage. "Backup power response capability risk" refers to the risk that backup generators, etc., can provide the required power in a timely and stable manner when needed, including factors such as start-up time and power ramp-up rate. Quantifying these risks provides a data foundation for refined system management.

[0015] This application provides an integrated power system resource scheduling method. First, the method includes real-time sensing of grid connection risks, energy storage unit health risks, and backup power response capability risks faced by the integrated power system during operation. Then, it quantifies these risks to obtain quantified grid connection risk indices, energy storage unit health risk indices, and backup power response capability risk indices. In one implementation, a sensor network can be deployed to collect real-time operational data such as grid instantaneous power, voltage, frequency, energy storage unit temperature, charging and discharging current, backup power fuel reserves, and startup status. For example, grid connection risks can be assessed by monitoring parameters such as the grid instantaneous power change rate and voltage fluctuation amplitude; energy storage unit health risks can be assessed by monitoring parameters such as the temperature, cycle count, and internal resistance of the energy storage units; and backup power response capability risks can be assessed by monitoring parameters such as the backup power startup time, available fuel quantity, and maintenance records. This raw data is then input into a preset quantification model, such as one based on fuzzy logic, an expert system, or a machine learning model, to transform complex operating states into risk indices that are easy to compare and process. For example, when the instantaneous power change rate of the power grid exceeds a certain threshold, the grid access risk index will increase accordingly; when the temperature of the energy storage unit continues to rise and approaches the safety limit, the health risk index of the energy storage unit will increase significantly.

[0016] Secondly, this method involves prioritizing various loads within the service area of ​​the integrated power system to obtain an average priority weight for each load type. Specifically, loads can be divided into different levels; for example, life-saving loads (such as hospitals and fire protection systems) are assigned the highest priority, followed by production loads (such as factory production lines), and non-critical loads (such as office lighting and air conditioning) have the lowest priority. This hierarchical management can be implemented manually, based on historical data analysis, or in conjunction with user agreements. For example, in an industrial park, the load priority of the core production line can be set to 10, while the priority of the lighting load in the office area can be set to 3. The average priority weight can be calculated by averaging the priorities of multiple sub-loads of the same category, or by using a weighted average method that considers factors such as load duration and power output.

[0017] Furthermore, this method involves calculating a comprehensive risk value based on quantified grid connection risk indices, energy storage unit health risk indices, backup power response capability risk indices, and the average priority weights of various loads. The comprehensive risk value is a key indicator for measuring the overall operational risk of an integrated power system. For example, the comprehensive risk value can be calculated by weighted summation of the aforementioned risk indices and load priority weights. The weighting coefficients can be dynamically adjusted based on actual system operating experience, expert knowledge, or through optimization algorithms. For instance, in areas with frequent grid fluctuations, the weight of the grid connection risk index can be appropriately increased; in systems with severely aging energy storage equipment, the weight of the energy storage unit health risk index can be increased.

[0018] Next, the method involves comparing the overall risk value with a preset overall risk threshold, and triggering an emergency rescheduling mode when the overall risk value exceeds the threshold. The overall risk threshold is the dividing line between normal system operation and an emergency state, and can be set according to the system's design goals, safety standards, and economic requirements. For example, when the overall risk value reaches 70%, the system may trigger an emergency rescheduling mode. Once the overall risk value exceeds this threshold, it indicates that the risk faced by the system has reached an unacceptable level, requiring immediate intervention.

[0019] The method then includes dynamically adjusting various resource scheduling strategies of the integrated power system under emergency rescheduling mode based on the risk type contributing most among the grid access risk index, energy storage unit health risk index, and backup power response capability risk index. For example, if the grid access risk index contributes most, indicating severe grid fluctuations or excessively high electricity purchase costs, the system may prioritize adjusting its grid purchase strategy, such as reducing the amount purchased and utilizing local energy storage or backup power sources more. If the energy storage unit health risk index contributes most, the system may prioritize adjusting the energy storage unit's charging and discharging strategy, such as reducing discharge power or even suspending discharge to protect the energy storage device. If the backup power response capability risk index contributes most, the system may pre-start the backup power source for preheating or adjust its output plan to ensure timely response when needed.

[0020] Finally, this method involves continuously monitoring the operating status of the integrated power system after dynamically adjusting various resource scheduling strategies, and iteratively adjusting the resource scheduling strategies based on changes in the operating status until the comprehensive risk value falls below the comprehensive risk threshold. This means that the scheduling process is not completed in one go, but is a continuous feedback and optimization process. For example, after adjusting the strategy, the system will continue to monitor various risk indices and load priority weights, and recalculate the comprehensive risk value. If the comprehensive risk value is still higher than the threshold, the system will further adjust the scheduling strategy based on the latest risk assessment results. This iterative process will continue until the system returns to a safe and stable operating state.

[0021] The integrated power system resource scheduling method proposed in this application can effectively cope with the uncertainties in the operation of integrated power systems by introducing multi-dimensional risk quantification, load priority hierarchical management, and emergency rescheduling mechanisms. Traditional methods often focus on prediction-based optimization scheduling, but when faced with sudden high load events, grid fluctuations, or equipment health risks, their preset scheduling plans may fail, forcing the system to take costly or damaging measures.

[0022] This application further proposes a method for calculating the comprehensive risk value based on a quantitative grid access risk index, an energy storage unit health risk index, a backup power supply response capability risk index, and the average priority weights of various loads. The steps include: Real-time monitoring of the instantaneous power change rate of the power grid, the temperature rise rate of the energy storage unit, and the startup time of the backup power supply; The weighting coefficients of the grid access risk index, energy storage unit health risk index, and backup power response capability risk index are dynamically adjusted based on the grid instantaneous power change rate, energy storage unit temperature rise rate, and backup power start-up time. The adjusted weighting coefficients are weighted and summed with the quantified grid access risk index, energy storage unit health risk index, backup power response capability risk index, and the average priority weight of various loads to obtain the comprehensive risk value.

[0023] Specifically, real-time monitoring refers to continuously acquiring key operating parameters of the integrated power system, such as the instantaneous power change rate of the grid, the temperature rise rate of the energy storage unit, and the start-up time of the backup power supply, through sensors, SCADA systems, or other data acquisition equipment. Among these, the instantaneous power change rate reflects the stability of grid connection, the temperature rise rate of the energy storage unit is an important indicator of its health status, and the start-up time of the backup power supply is directly related to its responsiveness. Furthermore, based on these real-time monitored parameters, the weighting coefficients of the grid connection risk index, the energy storage unit health risk index, and the backup power supply responsiveness risk index are dynamically adjusted. For example, when the instantaneous power change rate of the grid increases sharply, it indicates a significant increase in grid connection risk, and the weighting coefficient of the grid connection risk index can be increased accordingly; when the temperature rise rate of the energy storage unit is too fast, the weighting coefficient of the energy storage unit health risk index is increased; and when the backup power supply start-up time exceeds the normal range, the weighting coefficient of the backup power supply responsiveness risk index is increased. This dynamic adjustment ensures that risk assessment can respond promptly to changes in the internal and external environment of the system. Finally, the adjusted weighting coefficients are weighted and summed with the quantified grid access risk index, energy storage unit health risk index, backup power response capability risk index, and the average priority weight of various loads to obtain a more accurate and real-time comprehensive risk value.

[0024] This application's solution addresses the static nature of the basic scheme's comprehensive risk value calculation by introducing real-time monitoring of the grid's instantaneous power change rate, the energy storage unit's temperature rise rate, and the backup power supply's startup time. Based on this real-time data, the weighting coefficients of each risk index are dynamically adjusted. Specifically, when the integrated power system faces an increased threat from a specific risk type (such as grid fluctuations, energy storage overheating, or slow backup power supply response), the corresponding risk index weighting coefficient is dynamically increased, thus increasing the contribution of that risk type to the comprehensive risk value. This mechanism ensures that the comprehensive risk value more sensitively and accurately reflects the system's most prominent current risk situation, avoiding the lag or inaccuracy in risk assessment caused by fixed weights. It is precisely this dynamic adjustment that enables the comprehensive risk value to more realistically characterize the overall risk level of the integrated power system, providing a reliable decision-making basis for subsequent emergency redistribution.

[0025] In some preferred embodiments, it is assumed that during normal operation of the integrated power system, the instantaneous power change rate of the grid, the temperature rise rate of the energy storage unit, and the standby power start-up time are all within preset safety thresholds. In this case, the weighting coefficients of each risk index may be set to a baseline value. However, when a sudden grid failure causes a rapid increase in the instantaneous power change rate, for example, from 0.1 MW / s to 10 MW / s, the system will immediately detect this change. Based on this real-time data, the weighting coefficient of the grid access risk index will be dynamically adjusted from a baseline value (e.g., 0.3) to a higher value (e.g., 0.6) to reflect the significant increase in grid access risk. Simultaneously, the weighting coefficients of the energy storage unit health risk index and the standby power response capability risk index may be reduced or remain unchanged accordingly. In this way, the contribution of grid access risk will increase significantly when calculating the comprehensive risk value. Even if other risk indices do not change significantly, the comprehensive risk value may quickly exceed the preset comprehensive risk threshold, thereby triggering an emergency reschedule mode in a timely manner. This prompts the system to prioritize grid access risk, such as stabilizing grid power through rapid response of the energy storage unit or isolating affected loads. For example, if at some point the cooling system of an energy storage unit malfunctions, causing its temperature to rise at an increasingly rapid rate, exceeding a safety threshold, the weighting coefficient of the energy storage unit's health risk index will be dynamically increased, raising its proportion in the overall risk value. Even if the grid operates smoothly and backup power is on standby, the increased weighting of the energy storage unit's health risk may still cause the overall risk value to rise rapidly, triggering emergency redistribution. This prompts the system to take measures to protect the energy storage unit, such as limiting its charging and discharging power or temporarily disconnecting it from operation. These examples clearly demonstrate that by monitoring key operating parameters in real time and dynamically adjusting weighting coefficients, the overall risk value can more accurately reflect the actual risk situation faced by the integrated power system, thereby achieving smarter and more timely risk management and resource scheduling.

[0026] This application further proposes steps for dynamically adjusting various resource scheduling strategies of an integrated power system based on the risk type that contributes the most among the grid connection risk index, energy storage unit health risk index, and backup power response capability risk index. These steps include: Identify the risk types that contribute the most to the grid connection risk index, energy storage unit health risk index, and backup power response capability risk index; When multiple risk indices contribute at similar levels, calculate the risk correlation coefficient between similar risk types; Based on the risk correlation coefficient, dynamically adjust the priority of similar risk types; Based on the adjusted priorities, a multi-objective optimized scheduling strategy is generated and executed.

[0027] Specifically, identifying the risk type that contributes the most among the grid connection risk index, energy storage unit health risk index, and backup power response capability risk index means determining the risk type that has the greatest impact on the overall risk value of the integrated power system by comparing the currently quantified values ​​of these indices. For example, a threshold can be set; when a certain risk index exceeds this threshold and is significantly higher than other risk indices, it is identified as the risk type that contributes the most.

[0028] When multiple risk indices contribute at roughly the same level, it can be understood that the numerical differences between these indices are within a preset tolerance range; for example, their values ​​differ from each other by no more than a certain percentage or absolute value. In this case, for more precise scheduling, it is necessary to calculate the risk correlation coefficient between closely related risk types. The risk correlation coefficient aims to quantify the degree of mutual influence between different risk types. For example, will an increase in grid connection risk exacerbate the health risks of energy storage units, or will insufficient backup power supply response amplify grid connection risk? This correlation coefficient can be obtained through historical data analysis, expert experience, or simulation calculations based on physical models.

[0029] In practical applications, dynamically adjusting the priority of similar risk types based on risk correlation coefficients means that among multiple risk types with similar contributions, their mutual influence is considered to reassess and determine their order of importance in resource scheduling. For example, if the correlation coefficient between grid connection risk and energy storage unit health risk is high, and their contributions are similar, then during scheduling, both risks may be mitigated simultaneously, or one may be given higher priority based on the strength of their interaction.

[0030] Therefore, based on the adjusted priorities, a multi-objective optimized scheduling strategy is generated and executed. This strategy considers multiple risk mitigation objectives simultaneously during the scheduling process; for example, while reducing grid connection risks, it also considers the health maintenance of energy storage units and the availability of backup power. Through optimization algorithms, this strategy seeks a resource allocation scheme that achieves the optimal balance among multiple objectives while satisfying system operational constraints.

[0031] This application's solution addresses the limitations of scheduling based solely on a single, highest-contribution risk type by introducing a refined processing mechanism for multiple risk types with similar contributions. Specifically, when an integrated power system faces a complex situation where multiple risk indices contribute at similar levels, these similar risk types are first identified. Subsequently, by calculating the risk correlation coefficients between these similar risk types, a deeper understanding of their interactions and potential chain reactions can be achieved, avoiding the negative impacts of treating a single risk in isolation. Based on these correlation coefficients, the system can dynamically adjust the priority of these similar risk types, enabling scheduling decisions to more accurately reflect the actual risk landscape faced by the system. Finally, by generating and executing a multi-objective optimized scheduling strategy, the integrated power system can simultaneously consider multiple risk mitigation objectives, achieving more comprehensive and robust resource scheduling. This effectively avoids the "whack-a-mole" phenomenon, ensuring stable system operation in complex risk environments.

[0032] In some preferred embodiments, it is assumed that the integrated power system enters emergency rescheduling mode at a certain moment. At this time, the system monitors a grid access risk index of 0.75, an energy storage unit health risk index of 0.72, and a backup power response capability risk index of 0.70. The contributions of these three risk indices are all high and close to each other.

[0033] First, the system identified that all three risk types belong to risks with similar contributions.

[0034] Next, the system calculates the correlation coefficient between grid connection risk and energy storage unit health risk, as well as the correlation coefficients between grid connection risk and backup power response capability risk, and between energy storage unit health risk and backup power response capability risk. For example, historical data analysis reveals that a sharp increase in grid connection risk often leads to frequent charging and discharging of energy storage units, thereby accelerating their health degradation; therefore, the correlation coefficient between grid connection risk and energy storage unit health risk is relatively high.

[0035] Based on these correlation coefficients, the system dynamically adjusts the priority of these three risks. For example, if the correlation coefficient between grid connection risk and energy storage unit health risk is the highest, the system may appropriately increase the priority of grid connection risk and energy storage unit health risk to ensure that both risks are mitigated simultaneously during scheduling.

[0036] Ultimately, based on the adjusted priorities, the system generates a multi-objective optimized scheduling strategy. This strategy may include: moderately reducing the charging and discharging power of energy storage units to protect their health while ensuring grid stability, and simultaneously reserving sufficient backup power capacity to cope with potential further risks. For example, the scheduling strategy may instruct energy storage units to discharge at lower power for short periods to smooth grid fluctuations, while simultaneously starting some backup generators to provide additional support and limiting power supply to non-critical loads to ensure the stable operation of critical loads. In this way, the system can achieve coordinated management and optimization of multiple risks, thereby more effectively addressing complex operational challenges.

[0037] This application further proposes a step of continuously monitoring the operating status of the integrated power system after dynamically adjusting various resource scheduling strategies, and iteratively adjusting the resource scheduling strategies according to changes in the operating status until the comprehensive risk value falls below the comprehensive risk threshold. This specifically includes the following steps: Based on the rate of change of the comprehensive risk value and the contribution of each risk index, the data collection frequency of the operating status is dynamically adjusted to obtain the adjusted data collection frequency. Based on the adjusted data collection frequency, operational status data is collected, and based on the collected operational status data and system response data under historical high load events, the evolution trend of each risk index within a short time window is predicted, thus obtaining the predicted evolution trend. The process of reducing the overall risk value to below the overall risk threshold is divided into multiple recovery phases. In each recovery phase, the resource scheduling strategy is iteratively adjusted based on the current recovery phase and the predicted evolution trend until the overall risk value falls below the overall risk threshold.

[0038] Specifically, after the integrated power system enters emergency rescheduling mode and executes the initial scheduling strategy, the system needs to continuously monitor its operating status to ensure effective risk control and gradual system recovery. To improve the efficiency and accuracy of monitoring, the frequency of data collection for operating status is not fixed but dynamically adjusted. The rate of change of the comprehensive risk value refers to the speed at which the comprehensive risk value rises or falls within a short period, reflecting the dynamic nature of the system's risk state. The contribution of each risk index represents the degree of influence of the grid access risk index, the energy storage unit health risk index, and the backup power response capability risk index on the current comprehensive risk value. When the rate of change of the comprehensive risk value is rapid or the contribution of a certain risk index is high, it indicates that the system is in an unstable or critical state. In this case, the data collection frequency will be increased accordingly to obtain more intensive and real-time operating data. Conversely, when the system tends to be stable or the risk contribution is low, the collection frequency can be appropriately reduced to decrease the data processing burden.

[0039] Furthermore, after obtaining the adjusted data acquisition frequency, the system will collect operational status data at that frequency. This collected real-time operational status data, such as instantaneous grid power, energy storage unit temperature, standby generator fuel reserves, and actual power at each load point, will be analyzed in conjunction with system response data from historical high-load events. This historical system response data from high-load events provides valuable experience on how the system responds and evolves under similar high-risk or high-load scenarios. Based on this data, predictive models (e.g., based on machine learning algorithms or time series analysis models) can be used to predict the evolution trends of various risk indices within short-term time windows. This prediction can provide forward-looking guidance for subsequent resource scheduling strategy adjustments, making scheduling decisions more predictable.

[0040] Furthermore, to more precisely manage the system's recovery from a high-risk state to a normal state, the process of the overall risk value falling below the overall risk threshold is divided into multiple recovery phases. For example, these can be divided into an "emergency stabilization phase," a "resource optimization phase," and a "system fine-tuning phase." Each recovery phase has different objectives and focuses. In each recovery phase, the iterative adjustment of resource scheduling strategies comprehensively considers the characteristics of the current recovery phase and the predicted evolution trend. For example, in the emergency stabilization phase, the strategy may focus more on quickly suppressing the spread of risk; while in the resource optimization phase, it may focus more on optimizing resource utilization efficiency. Through this phased, prediction-based iterative adjustment, the system can more effectively guide the overall risk value to gradually fall below the overall risk threshold.

[0041] In some preferred embodiments, it is assumed that the integrated power system experiences a sudden grid failure, causing a sharp increase in the grid access risk index and triggering an emergency reschedule mode. At this point, the comprehensive risk value exceeds a preset comprehensive risk threshold. According to the scheme of this application, the system will first detect the rapid increase in the comprehensive risk value and identify the grid access risk index as the main contributor. Therefore, the frequency of collecting operating status data will be dynamically increased, for example, from once per minute to once every 10 seconds, to more intensively acquire key data such as instantaneous grid power, voltage frequency, etc. At the same time, the system will use the collected real-time data and historical system response data under grid failures to predict the evolution trend of the grid access risk index within the next 5 minutes, for example, predicting that it may continue to fluctuate in a short period of time and then gradually stabilize. Based on this prediction, the system divides the recovery process into "emergency isolation and initial stabilization stage", "load transfer and backup power start-up stage" and "grid recovery and system optimization stage". In the "emergency isolation and initial stabilization stage", the dispatch strategy will prioritize cutting off some non-critical loads and start backup power to quickly reduce grid access risk. During the "load transfer and backup power startup phase," based on the predicted risk evolution trend, the system will gradually transfer critical loads to backup power and optimize the output of the backup power. During the "grid restoration and system optimization phase," as the grid connection risk index further decreases, the system will gradually switch loads back to the grid and optimize the charging and discharging of energy storage units to ensure stable system operation. In each phase, the system will iteratively adjust its resource scheduling strategy based on the latest operational status data and predicted trends until the overall risk value finally falls below the overall risk threshold, and the system returns to normal operation.

[0042] This application further proposes steps for dynamically adjusting various resource scheduling strategies of an integrated power system based on the risk type that contributes the most among the grid connection risk index, energy storage unit health risk index, and backup power response capability risk index. These steps include: Identify the risk types that contribute the most to the grid connection risk index, energy storage unit health risk index, and backup power response capability risk index; Generate an initial single resource scheduling strategy based on the risk type that contributes the most. The initial single resource scheduling strategy was simulated and the expected changes in key operating parameters in the integrated power system were monitored to obtain the chain reaction of the initial single resource scheduling strategy on other non-major risk types. Based on the severity of the cascading effects and the priority of the affected risk types, the initial single resource scheduling strategy is modified to generate a global optimized scheduling strategy, which is then executed.

[0043] Specifically, identifying the risk type that contributes most to the grid connection risk index, energy storage unit health risk index, and backup power response capability risk index means, under the current operating state of the integrated power system, comprehensively assessing and determining the risk category with the greatest impact on the comprehensive risk value by analyzing multiple dimensions such as the weight, rate of change, and ratio of the current value to the historical maximum value of each risk index in the calculation of the comprehensive risk value. Among these, generating an initial single resource scheduling strategy based on the risk type that contributes most means initially formulating a scheduling plan aimed at directly mitigating the identified risk type. For example, if the grid connection risk index contributes most, the initial strategy might include adjusting the power output of the grid-connected inverter and requesting grid-side support; if the energy storage unit health risk index contributes most, it might involve adjusting the charging and discharging power of the energy storage unit and limiting its deep cycling; if the backup power response capability risk index contributes most, it might include starting the backup generator in advance and adjusting its operating load.

[0044] Furthermore, simulating the execution of the initial single-resource scheduling strategy and monitoring the expected changes in key operating parameters of the integrated power system reveals the cascading effects of the initial single-resource scheduling strategy on other non-primary risk types. This refers to predicting the implementation effect of the initial single-resource scheduling strategy through an established system model without actual intervention in system operation. Key operating parameters may include instantaneous grid power, energy storage unit temperature, standby generator fuel reserves, and actual power at each load point. By monitoring the expected changes in these parameters, the potential positive or negative impacts of the initial strategy on other non-primary risk types (i.e., risk types with relatively small contributions) can be assessed. For example, significantly adjusting energy storage charging and discharging to mitigate grid connection risks may lead to excessively high energy storage unit temperatures, thereby exacerbating energy storage unit health risks.

[0045] Therefore, based on the severity of the cascading effects and the priority of the affected risk types, the initial single resource scheduling strategy is modified to generate a globally optimized scheduling strategy, which is then executed. The severity of the cascading effects can be quantified by assessing the magnitude and duration of changes in key operating parameters and their potential impact on relevant risk indices. The priority of affected risk types refers to the different requirements for system stability and reliability imposed by different load types within the integrated power system service area, resulting in varying degrees of importance for their corresponding risk types. Based on these assessment results, the initial single resource scheduling strategy is modified. For example, while mitigating major risks, resource allocation is appropriately adjusted to reduce or avoid negative impacts on other high-priority non-major risks, thus forming a globally optimized scheduling strategy that can consider the overall system risk.

[0046] In some preferred embodiments, it is assumed that the integrated power system faces a situation where the grid connection risk index increases significantly at a certain moment, for example, due to excessive instantaneous power change rate caused by external grid fluctuations. In this case, grid connection risk is identified as the risk type that contributes the most. Based on this risk, the system initially generates an initial single-resource scheduling strategy, which may instruct the energy storage unit to discharge to the grid at maximum power to quickly stabilize the grid connection point. However, when simulating the execution of this initial strategy, the system monitors that the temperature rise rate of the energy storage unit is expected to increase significantly, indicating that the energy storage unit health risk index may reach a dangerous level in a short period of time, thereby adversely affecting the long-term operating life of the energy storage unit. At this time, the energy storage unit health risk is identified as a non-primary risk type affected by cascading effects, and its priority is high. Based on the severity of this cascading effect and the priority of the energy storage unit health risk, the system will revise the initial single-resource scheduling strategy. For example, the revised global optimized scheduling strategy may be adjusted to: the energy storage unit discharges at a level slightly below maximum power while starting a backup generator to provide partial support, or the cooling system is started to control its temperature while the energy storage unit is discharging. In this way, the risks of grid connection are effectively mitigated, and the excessive impact on the health risks of energy storage units is avoided, thus achieving a balanced optimization of the overall system risk.

[0047] This application further proposes that the steps for obtaining the cascading effects of the initial single resource scheduling strategy on other non-primary risk types may include the following: Real-time monitoring of key operating parameters in an integrated power system includes at least the instantaneous power of the grid, the temperature of the energy storage unit, the fuel balance of the standby generator, and the actual power of each load point. Based on changes in key operating parameters, the coupling strength among the grid connection risk index, energy storage unit health risk index, and backup power response capability risk index is calculated. Based on the coupling strength, predict the combined evolution path of grid access risk, energy storage unit health risk, and backup power response capability risk; Based on the complex evolution path, assess the cascading effects of the initial single resource scheduling strategy on other non-primary risk types.

[0048] Specifically, real-time monitoring of key operating parameters in an integrated power system, including at least the instantaneous power of the grid, the temperature of the energy storage unit, the fuel reserve of the standby generator, and the actual power at each load point, refers to the continuous and real-time collection of core indicators reflecting the system's operating status and potential risks through various sensors, smart meters, and SCADA (Supervisory Control and Data Acquisition) systems deployed within the integrated power system. Changes in the instantaneous power of the grid are directly related to grid connection risks; for example, grid fluctuations or faults may cause drastic power fluctuations. The temperature of the energy storage unit is an important indicator of its health and safe operation; excessively high or low temperatures can affect its performance and lifespan. The fuel reserve of the standby generator directly determines the continuous response capability of the backup power supply. Changes in the actual power at each load point reflect the dynamic nature of the system's load demand. Real-time monitoring of these key operating parameters allows for the acquisition of actual operating data of the integrated power system during the initial execution of a single resource scheduling strategy, providing a foundation for subsequent risk assessment.

[0049] The calculation of the coupling strength among the grid connection risk index, energy storage unit health risk index, and backup power response capability risk index, based on changes in key operating parameters, refers to quantifying the degree of mutual influence between different risk types by establishing mathematical models or employing data analysis methods after obtaining data on changes in key operating parameters. For example, drastic fluctuations in instantaneous grid power may lead to frequent charging and discharging of energy storage units, thereby affecting their temperature and health status, demonstrating the coupling between grid connection risk and energy storage unit health risk. Similarly, the start-up of backup generators may consume fuel; insufficient fuel reserves will affect their response capability and may increase dependence on the grid, demonstrating the coupling between backup power response capability risk and grid connection risk. The calculation of coupling strength can employ methods such as correlation analysis, grey relational analysis, mutual information theory, or causal inference models based on machine learning to reveal the interaction relationships between various risk indices under parameter changes.

[0050] In practical applications, predicting the composite evolution path of grid connection risk, energy storage unit health risk, and backup power response capability risk based on coupling strength refers to using a predictive model to forecast the comprehensive change trend of each risk index after the execution of an initial single resource scheduling strategy, after clarifying the coupling relationship between various risk indices. The composite evolution path not only considers the changes of individual risk indices, but more importantly, it incorporates the coupling effects between them, thus simulating how a change in one risk triggers or exacerbates changes in other risks, forming a dynamic, multi-dimensional risk evolution picture. Predictive models can include time series analysis, Kalman filtering, neural network models, or simulations based on physical models, combining historical data, expert experience, and real-time monitoring data to extrapolate the comprehensive development trend of each risk index within a short future time window.

[0051] Therefore, assessing the cascading effects of an initial single resource scheduling strategy on other non-primary risk types, based on the complex evolution path, involves comparing the predicted complex evolution path with preset risk thresholds or safe operating boundaries to determine whether other non-primary risks will worsen after the initial single resource scheduling strategy is implemented, whether they will exceed acceptable limits, and the extent of the worsening. The cascading effect refers to the potential positive, negative, or neutral collateral effects that the initial single resource scheduling strategy may have on other non-primary risk types while addressing the primary risk. By assessing the complex evolution path, potential risk transfer or risk amplification effects can be identified. For example, a strategy aimed at rapidly reducing grid connection risks may lead to excessive discharge of energy storage units, thereby accelerating the accumulation of their health risks. This assessment helps to comprehensively understand the overall impact of the strategy and avoid a situation where "pressing down one problem only creates another."

[0052] This application's solution obtains real-time data on the system's actual operating status by monitoring key operating parameters in an integrated power system. Based on this data, the coupling strength between different risk types is calculated, revealing the intrinsic mechanism of their mutual influence. Furthermore, the coupling strength is used to predict the composite evolution path of each risk index after strategy execution, making the assessment of future risk trends more comprehensive and accurate. Finally, based on the predicted composite evolution path, the cascading effects that an initial single resource scheduling strategy may have on other non-primary risk types can be quantitatively assessed. This series of steps works together to expand the assessment of the scheduling strategy's impact from a single dimension to multiple dimensions, and from static assessment to dynamic prediction, thereby enabling a deeper understanding of the strategy's potential risks and benefits.

[0053] This application further proposes steps for generating a globally optimized scheduling strategy, including: The dynamic response characteristics of various types of resources in the integrated power system are obtained. These dynamic response characteristics include the charging and discharging response time of the energy storage unit, the start-up time of the backup generator, and the power ramp-up rate of the backup generator. Based on dynamic response characteristics, the timing sequence of various types of resource allocation instructions is arranged to obtain the timing sequenced resource allocation instructions. Based on the time-series orchestrated resource allocation instructions, the initial single resource scheduling strategy is modified to generate a globally optimized scheduling strategy. In the initial stage of the global optimization scheduling strategy, monitor the deviation between the actual output power and the expected output power of each type of resource; Adjust subsequent instructions based on the deviation.

[0054] Specifically, acquiring the dynamic response characteristics of various types of resources in an integrated power system means that the system needs to accurately understand the time and process required for each type of energy device it manages (such as energy storage units, backup generators, etc.) to go from a static state to reaching the target output state after receiving a dispatch command. Specifically, the charging and discharging response time of an energy storage unit refers to the time required from receiving a charging and discharging command to actually starting charging and discharging or reaching the set charging and discharging power; the start-up time of a backup generator refers to the time required from receiving a start-up command to stabilizing its output power; and the power ramp-up rate of a backup generator refers to the rate at which its power output increases per unit time after startup. These characteristics form the basis for developing efficient and feasible dispatch strategies, aiming to ensure that dispatch commands match the response capabilities of the actual physical equipment.

[0055] Furthermore, based on dynamic response characteristics, the resource allocation instructions of various types are time-series orchestrated to obtain time-series orchestrated resource allocation instructions. This means that when generating scheduling instructions, it is no longer a simple allocation of power or energy, but rather a fine-grained arrangement of the instruction issuance time, duration, and target value, taking into account the response time of each resource. For example, for standby generators requiring a long start-up time, their start-up instructions may need to be issued earlier; for energy storage units with rapid response, their charging and discharging instructions can be adjusted more flexibly. The purpose of time-series orchestration is to maximize the utilization of the advantages of each resource and avoid system instability or resource waste caused by untimely response.

[0056] Therefore, based on the time-series orchestrated resource allocation instructions, the initial single resource scheduling strategy is modified to generate a globally optimized scheduling strategy. This means that after considering the initial strategy under a single risk type and its cascading effects, the dynamic response characteristics of resources and time-series orchestration are further integrated into the strategy, forming a comprehensive scheduling scheme that can address major risks, take into account other risks, and is executable in the time dimension.

[0057] Furthermore, in the initial stage of implementing the global optimization scheduling strategy, the deviation between the actual output power and the expected output power of each type of resource is monitored. This step aims to introduce a real-time feedback mechanism, as uncertainties or model errors may still exist in actual operation, even with a carefully designed strategy. Continuous monitoring can promptly identify differences between the actual output and the expected output.

[0058] Finally, subsequent instructions are adjusted based on the deviation. When a deviation is detected between the actual output and the expected output, the system can react quickly and correct the scheduling instructions that have not yet been executed to rectify the deviation and ensure that the system operation always progresses in the expected direction. The purpose is to improve the adaptability and robustness of the scheduling strategy.

[0059] This application's solution first acquires the dynamic response characteristics of various types of resources in the integrated power system, enabling the scheduling system to fully understand the physical limitations and response capabilities of the controlled devices. This precise understanding of these characteristics allows for refined timing orchestration of subsequent resource allocation instructions, thus avoiding scheduling failures or inefficiencies caused by device response delays. Based on this, the timing-orchestrated instructions are integrated into the generation of a global optimization scheduling strategy, ensuring a balance between the theoretical optimization and practical feasibility of the strategy. Furthermore, by introducing real-time monitoring of the deviation between actual and expected output power at the initial stage of strategy execution, this application's solution can promptly detect and quantify uncertainties or external disturbances in strategy execution. This real-time feedback mechanism allows the system to dynamically adjust subsequent instructions based on the deviation, forming a closed-loop, adaptive scheduling process. This effectively addresses the complex dynamic changes in the integrated power system's operation, ensuring that resources can be efficiently and accurately mobilized in emergency rescheduling mode to quickly reduce overall risk.

[0060] In some preferred embodiments, it is assumed that the integrated power system, in emergency rescheduled mode, needs to rapidly increase its power supply capacity to cope with grid connection risks. In this case, the system first obtains the charge / discharge response time of its energy storage units (e.g., 50 milliseconds from command issuance to full-power discharge), the start-up time of the backup generator (e.g., 5 minutes from command issuance to stable output), and the power ramp-up rate (e.g., 1 megawatt per minute). Based on these dynamic response characteristics, the system performs timing orchestration. For example, to achieve stable output from the backup generator after 5 minutes, its start-up command is issued immediately. Simultaneously, to compensate for power gaps during backup generator start-up, the discharge commands of the energy storage units are precisely scheduled at specific points in time after backup generator start-up and continue for a certain duration for a smooth transition.

[0061] In the initial stage of executing the global optimization scheduling strategy, the system continuously monitors the actual discharge power of the energy storage unit and the actual output power of the standby generator. For example, if it is found that the power ramp-up rate of the standby generator is lower than expected during startup, resulting in a deviation between the actual and expected output power, the system will immediately adjust the subsequent discharge commands of the energy storage unit based on this deviation, such as extending the discharge time or slightly increasing the discharge power, to compensate for the standby generator's shortcomings. In this way, the solution of this application can ensure that the resource scheduling strategy of the integrated power system remains efficient and reliable in a dynamically changing operating environment, thereby effectively responding to emergencies.

[0062] This application further proposes steps for monitoring the deviation between the actual output power and the expected output power of various types of resources, including: Continuously monitor the deviation between the actual output power and the expected output power of each type of resource, and record the duration, rate of change and magnitude of each deviation; Calculate the dynamic risk weight of each deviation based on its duration, rate of change, and magnitude. Based on the dynamic risk weights of each deviation and combined with the coupling relationship between different types of resources, the evolution trend of the composite deviation of the output power of different types of resources within a short time window is predicted. Based on the evolution trend of the composite deviation, determine whether subsequent instructions need to be adjusted, and generate adjustment instructions.

[0063] Specifically, continuously monitoring the deviation between the actual and expected output power of various types of resources, and recording the duration, rate of change, and magnitude of each deviation, refers to the system's uninterrupted tracking of the differences between the actual output power of all types of resources (such as energy storage units, backup generators, etc.) in the integrated power system and the preset or planned expected output power. The duration can be understood as the length of time from when the deviation is first detected until it returns to a normal or stable state; the rate of change refers to how quickly the deviation value changes over time, such as the change in power deviation per second; and the magnitude refers to the maximum absolute value of the deviation or its average absolute value over a certain period. Recording these parameters aims to provide a comprehensive and detailed data foundation for subsequent risk assessment.

[0064] The dynamic risk weight calculation for each deviation, based on its duration, rate of change, and magnitude, refers to the quantitative assessment of the potential risk posed by each deviation to the operational stability and reliability of the integrated power system, using a pre-defined algorithm, model, or rule, based on the recorded deviation characteristics. For example, deviations with longer durations, faster rates of change, and larger magnitudes are typically assigned higher dynamic risk weights, indicating a greater threat to the system and requiring more urgent intervention. This dynamic risk weight can be a dimensionless numerical value used for prioritizing multiple deviations when they coexist.

[0065] In practical applications, predicting the composite deviation evolution trend of the output power of various resources within a short future time window, based on the dynamic risk weights of each deviation and the coupling relationships between different types of resources, refers to further considering the mechanisms of mutual influence between different resources in an integrated power system after assessing the risk of individual deviations. For example, the power output deviation of an energy storage unit may affect the operating status of other standby generators or load points through grid connections. Coupling relationships can be understood as the degree of interdependence or interaction between resources. By combining dynamic risk weights with these coupling relationships, the system can predict how these individual deviations will interact, superimpose, or amplify within a relatively short future time window (e.g., the next few minutes or tens of minutes), thus forming a more complex composite deviation evolution path. This prediction can employ time series analysis, machine learning models, or simulation methods based on physical models.

[0066] In some preferred embodiments, it is assumed that during the initial stage of implementing a global optimization scheduling strategy, the integrated power system detects that the actual discharge power of a certain energy storage unit is lower than the expected output power, and that the duration, rate of change, and magnitude of this deviation are increasing. The system first records these dynamic parameters. Subsequently, based on a preset risk assessment model, it calculates the dynamic risk weight of the energy storage unit's deviation. For example, the weight increases by 0.1 for every 1 second increase in duration; by 0.2 for every 1 MW / s increase in rate of change; and by 0.3 for every 1 MW increase in magnitude. It is assumed that the calculated dynamic risk weight is high. Simultaneously, the system analyzes the coupling relationship between the energy storage unit and adjacent load points and standby generators. For example, insufficient power from the energy storage unit may cause a voltage drop at adjacent load points and increase the starting pressure on standby generators. Based on this information, the system predicts that if no intervention is taken for the energy storage unit in the next 5 minutes, its power deviation will further increase, potentially triggering standby generator overload or an increase in the grid connection risk index. Based on this composite deviation evolution trend, the system determines that subsequent instructions need to be adjusted immediately and generates instructions, such as instructing the energy storage unit to increase the discharge power, while fine-tuning the priority of some controllable loads, and preparing to start a small backup generator to deal with potential cascading effects.

[0067] This application further proposes a procedure for calculating the dynamic risk weight of each deviation based on its duration, rate of change, and magnitude, including: Based on the duration, rate of change, and magnitude of each deviation, the duration, rate of change, and magnitude of each deviation are mapped to corresponding dynamic risk weights using a preset nonlinear function or lookup table method.

[0068] Specifically, the pre-defined nonlinear function refers to a mathematical model that reflects the nonlinear relationship between deviation parameters (duration, rate of change, and magnitude) and risk weights. For example, when the duration or magnitude of the deviation is small, the risk weight may show a slow growth trend; while when the deviation exceeds a certain threshold, the risk weight may grow rapidly exponentially or power-law-like to reflect a sharp deterioration in risk. This nonlinear function can be designed and calibrated based on historical data analysis, expert experience, or system simulation results. Common nonlinear functions include, but are not limited to, exponential functions, logarithmic functions, sigmoid functions, or polynomial functions. The lookup table method can be understood as mapping through a pre-established risk weight lookup table. This lookup table can be constructed based on a large amount of historical operating data, failure case analysis, and expert knowledge. For example, the duration, rate of change, and magnitude of the deviation can be divided into different intervals, and each interval combination corresponds to a pre-defined dynamic risk weight. When the deviation parameter is actually monitored, the system can quickly obtain the corresponding dynamic risk weight by querying this table. The lookup table method is particularly suitable for handling complex multidimensional nonlinear relationships and has high computational efficiency.

[0069] This application's solution calculates dynamic risk weights by introducing a pre-defined nonlinear function or a lookup table method, enabling a more accurate quantification of the risks arising from deviations between the actual and expected output power of various resource types. Because the duration, rate of change, and magnitude of these deviations often have a nonlinear impact on system stability, traditional linear weighting methods may fail to accurately reflect this complexity. The nonlinear function captures the accelerating or decelerating trend of risk changes with deviation parameters, assigning lower weights when deviations are small and rapidly increasing weights when deviations reach a critical point, thus more realistically reflecting the severity of the risk. Simultaneously, the lookup table method, based on rich historical experience and expert knowledge, provides validated risk weights for various deviation scenarios, avoiding the burden of complex real-time calculations and ensuring the accuracy and consistency of risk assessment. In this way, the calculated dynamic risk weights can more effectively guide subsequent command adjustments, ensuring that the integrated power system can respond more accurately and promptly to resource output deviations.

[0070] In some preferred embodiments, it is assumed that the actual discharge power of a certain energy storage unit in the integrated power system deviates from the expected discharge power. Specifically, to calculate the dynamic risk weight of this deviation, the following method can be used: Nonlinear function method: An exponential function can be preset, for example, risk weight = Aexp(B(duration + rate of change + magnitude)), where A and B are constants calibrated based on historical data and system characteristics. When the duration, rate of change, or magnitude of the deviation is small, the risk weight increases slowly; however, when these parameters exceed a certain threshold, the risk weight will increase exponentially. For example, when the deviation magnitude exceeds 5% and the duration exceeds 10 seconds, the risk weight may rapidly increase from 0.2 to 0.8 to reflect its serious threat to system stability.

[0071] Lookup table method: A three-dimensional lookup table can be constructed, with input dimensions being the duration of the deviation (e.g., 0-5s, 5-10s, >10s), the rate of change (e.g., 0%-1% / s, 1%-3% / s, >3% / s), and the magnitude (e.g., 0%-2%, 2%-5%, >5%). Each cell of the table stores a preset dynamic risk weight value. For example, when the duration is >10s, the rate of change is >3% / s, and the magnitude is >5%, the lookup table yields a dynamic risk weight of 0.95, indicating extremely high risk; while when the duration is 0-5s, the rate of change is 0%-1% / s, and the magnitude is 0%-2%, the lookup table yields a dynamic risk weight of 0.1, indicating low risk.

[0072] Using any of the above methods, the system can quickly and accurately calculate the corresponding dynamic risk weight based on the deviation parameters monitored in real time, providing a precise risk basis for subsequent adjustments to resource scheduling instructions.

[0073] refer to Figure 2 This application proposes an integrated power system resource scheduling system, applied to an integrated power system resource scheduling method. The system includes: The quantification module senses in real time the grid access risk, energy storage unit health risk and backup power response capability risk faced by the integrated power system during operation, and quantifies the grid access risk, energy storage unit health risk and backup power response capability risk to obtain quantified grid access risk index, energy storage unit health risk index and backup power response capability risk index. The management module performs importance priority classification management of various loads within the service area of ​​the integrated power system, and obtains the average priority weight of each type of load; The processing module calculates a comprehensive risk value based on the quantified grid access risk index, energy storage unit health risk index, backup power response capability risk index, and average priority weight of various loads. It compares the comprehensive risk value with a preset comprehensive risk threshold and triggers an emergency rescheduling mode when the comprehensive risk value exceeds the comprehensive risk threshold. The adjustment module, in emergency rescheduling mode, dynamically adjusts the various resource scheduling strategies of the integrated power system based on the risk type that contributes the most among the grid access risk index, energy storage unit health risk index, and backup power response capability risk index. The iterative module continuously monitors the operating status of the integrated power system after dynamically adjusting various resource scheduling strategies of the integrated power system, and iteratively adjusts the resource scheduling strategies according to changes in the operating status until the comprehensive risk value falls below the comprehensive risk threshold.

[0074] Specifically, the quantification module can be understood as the unit responsible for data acquisition and preliminary analysis. It acquires real-time operational data from the integrated power system through sensors and communication interfaces, such as grid voltage, frequency, energy storage unit charging and discharging status, temperature, and backup power supply operating status. It then processes this data using a pre-defined algorithm model to quantify the grid connection risk index, energy storage unit health risk index, and backup power supply response capability risk index. Its purpose is to provide accurate quantitative data for subsequent risk assessments.

[0075] The management module can be understood as a unit that classifies and prioritizes loads within the service area of ​​the integrated power system. This module can classify loads into different levels based on their nature (e.g., critical loads, important loads, general loads), their impact on system stability, and user needs, and calculate the average priority weight for each type of load. Its purpose is to prioritize the power supply needs of important loads during resource scheduling.

[0076] In practical applications, the processing module is the core unit responsible for risk assessment and mode triggering. This module receives various risk indices from the quantification module and load priority weights from the management module, and calculates the comprehensive risk value of the integrated power system based on these inputs. Subsequently, the processing module compares the calculated comprehensive risk value with a preset comprehensive risk threshold. Once the comprehensive risk value exceeds this threshold, the system is determined to be in a high-risk state, and an emergency rescheduling mode is immediately triggered. The purpose is to promptly identify system risks and activate the emergency response mechanism.

[0077] Furthermore, the adjustment module can be understood as the unit responsible for formulating and executing specific scheduling strategies in emergency rescheduling mode. Based on the risk type identified by the processing module as contributing the most, such as grid connection risk, energy storage unit health risk, or backup power response capability risk, this module dynamically adjusts the scheduling strategies of various resources (such as energy storage units, backup power, and controllable loads) in the integrated power system. Its purpose is to specifically address the most prominent risk issues and quickly restore system stability.

[0078] Furthermore, the iteration module can be understood as the unit responsible for continuously optimizing the scheduling strategy. After the adjustment module executes the initial scheduling strategy, the iteration module continuously monitors the operating status of the integrated power system, including changes in various risk indices, load response, and resource output. Based on these changes in operating status, the iteration module continuously iterates and adjusts the resource scheduling strategy until the overall risk value of the integrated power system falls below the preset overall risk threshold, ensuring that the system returns to a safe and stable operating state. Its purpose is to achieve closed-loop control and continuous optimization of the scheduling strategy.

[0079] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. An integrated power system resource scheduling method, characterized by, The method comprises the following steps: Real-time perception of the power grid access risk, energy storage unit health risk and backup power supply response capability risk in the operation process of the integrated power supply system, and quantification of the power grid access risk, energy storage unit health risk and backup power supply response capability risk to obtain a quantified power grid access risk index, energy storage unit health risk index and backup power supply response capability risk index; Importance priority classification management of various types of loads in the service area of the integrated power supply system is performed to obtain an average priority weight of various types of loads; Based on the quantified power grid access risk index, energy storage unit health risk index, backup power supply response capability risk index and average priority weight of various types of loads, a comprehensive risk value is calculated; The comprehensive risk value is compared with a preset comprehensive risk threshold value, and when the comprehensive risk value exceeds the comprehensive risk threshold value, an emergency rescheduling mode is triggered; In the emergency rescheduling mode, the resource scheduling strategy of the integrated power supply system is dynamically adjusted according to the risk type with the largest contribution among the power grid access risk index, energy storage unit health risk index and backup power supply response capability risk index; After the resource scheduling strategy of the integrated power supply system is dynamically adjusted, the operation state of the integrated power supply system is continuously monitored, and the resource scheduling strategy is iteratively adjusted according to the change of the operation state until the comprehensive risk value falls below the comprehensive risk threshold value.

2. The method of claim 1, wherein, The step of calculating the comprehensive risk value based on the quantified power grid access risk index, energy storage unit health risk index, backup power supply response capability risk index and average priority weight of various types of loads comprises: Real-time monitoring of the power grid instantaneous power change rate, energy storage unit temperature rise rate and backup power supply startup time; Dynamic adjustment of the weight coefficients of the power grid access risk index, energy storage unit health risk index and backup power supply response capability risk index according to the power grid instantaneous power change rate, energy storage unit temperature rise rate and backup power supply startup time; Weighted summation of the adjusted weight coefficients and the quantified power grid access risk index, energy storage unit health risk index, backup power supply response capability risk index and average priority weight of various types of loads to obtain the comprehensive risk value.

3. The method of claim 1, wherein the step of determining the power source system resource is performed by a power source system resource scheduler. The step of dynamically adjusting the resource scheduling strategy of the integrated power supply system according to the risk type with the largest contribution among the power grid access risk index, energy storage unit health risk index and backup power supply response capability risk index comprises: Identifying the risk type with the largest contribution among the power grid access risk index, energy storage unit health risk index and backup power supply response capability risk index; When the contribution degree of multiple risk indexes is close, a risk correlation coefficient between the close risk types is calculated; Dynamic adjustment of the priority of the close risk types according to the risk correlation coefficient; Based on the adjusted priority, a multi-objective optimization scheduling strategy is generated and executed.

4. The method of claim 1, wherein the step of determining the power source system resource is performed by a power source system resource scheduler. The step of continuously monitoring the operation state of the integrated power supply system after the resource scheduling strategy of the integrated power supply system is dynamically adjusted, and iteratively adjusting the resource scheduling strategy according to the change of the operation state until the comprehensive risk value falls below the comprehensive risk threshold value comprises: According to the change rate of the comprehensive risk value and the contribution degree of each risk index, the collection frequency of the operation state data is dynamically adjusted to obtain an adjusted data collection frequency; According to the adjusted data collection frequency, the operation state data is collected, and based on the collected operation state data and system response data under historical high-load events, the evolution trend of each risk index in a future short time window is predicted to obtain a predicted evolution trend; The process of returning the comprehensive risk value to below the comprehensive risk threshold is divided into multiple recovery stages, and in each recovery stage, the resource scheduling strategy is iteratively adjusted according to the recovery stage currently in and the predicted evolution trend, until the comprehensive risk value returns to below the comprehensive risk threshold.

5. The method of claim 1, wherein, The steps of dynamically adjusting the resource scheduling strategies of each type of integrated power supply system according to the risk type that contributes most to the grid access risk index, the energy storage unit health risk index, and the backup power supply response capability risk index include: Identifying the risk type that contributes most to the grid access risk index, the energy storage unit health risk index, and the backup power supply response capability risk index; According to the risk type that contributes most, an initial single-resource scheduling strategy is generated; Simulate the execution of the initial single-resource scheduling strategy, and monitor the expected changes of the key operating parameters in the integrated power supply system to obtain the cascading effects of the initial single-resource scheduling strategy on other non-main risk types; According to the severity of the cascading effects and the priority of the affected risk types, the initial single-resource scheduling strategy is corrected to generate a globally optimized scheduling strategy, and the globally optimized scheduling strategy is executed.

6. The method of claim 5, wherein, The steps of obtaining the cascading effects of the initial single-resource scheduling strategy on other non-main risk types include: Real-time monitoring of changes in key operating parameters in the integrated power supply system, including at least grid instantaneous power, energy storage unit temperature, backup generator fuel reserve, and actual power of each load point; According to the changes in the key operating parameters, the coupling strength between the grid access risk index, the energy storage unit health risk index, and the backup power supply response capability risk index is calculated; According to the coupling strength, the combined evolution path of the grid access risk, the energy storage unit health risk, and the backup power supply response capability risk is predicted; According to the combined evolution path, the cascading effects of the initial single-resource scheduling strategy on other non-main risk types are evaluated.

7. The method of claim 5, wherein the step of determining the power source system resource is performed by a power source system resource scheduler. The steps of generating a globally optimized scheduling strategy include: Obtaining the dynamic response characteristics of each type of resource in the integrated power supply system, including the charge / discharge response time of the energy storage unit, the startup time of the backup generator, and the power ramping rate of the backup generator; Based on the dynamic response characteristics, the timing arrangement of the resource allocation instructions is performed to obtain the timing-arranged resource allocation instructions; According to the timing-arranged resource allocation instructions, the initial single-resource scheduling strategy is corrected to generate a globally optimized scheduling strategy; In the early stage of the execution of the globally optimized scheduling strategy, the deviation between the actual output power and the expected output power of each type of resource is monitored; According to the deviation, the subsequent instructions are adjusted.

8. The method of claim 7, wherein the step of determining the power source system resource is performed by a power source system resource scheduler. The steps of monitoring the deviation between the actual output power and the expected output power of each type of resource include: Continuously monitor the deviation of the actual output power of each type of resource from the expected output power, and record the duration, rate of change and amplitude of each deviation; According to the duration, rate of change and amplitude of each deviation, calculate the dynamic risk weight of each deviation; According to the dynamic risk weight of each deviation, combined with the coupling relationship between each type of resource, predict the evolution trend of the composite deviation of the output power of each type of resource in the future short time window; According to the evolution trend of the composite deviation, judge whether it is necessary to adjust the subsequent instruction, and generate the adjustment instruction.

9. The method of claim 8, wherein the step of determining the power source system resource is performed by a power source system resource scheduler. The step of calculating the dynamic risk weight of each deviation according to the duration, rate of change and amplitude of each deviation comprises: According to the duration, rate of change and amplitude of each deviation, the duration, rate of change and amplitude of each deviation are mapped to the corresponding dynamic risk weight by a preset nonlinear function or lookup table method.

10. An integrated power system resource scheduling system, applied to the integrated power system resource scheduling method of claim 1, characterized in that, The system comprises: A quantification module that senses in real time the grid access risk, energy storage unit health risk and backup power response capability risk faced by the integrated power supply system during operation, and quantifies the grid access risk, energy storage unit health risk and backup power response capability risk to obtain a quantified grid access risk index, energy storage unit health risk index and backup power response capability risk index; A management module that performs importance priority classification management on various types of loads in the service area of the integrated power supply system to obtain the average priority weight of each type of load; A processing module that calculates a comprehensive risk value based on the quantified grid access risk index, energy storage unit health risk index, backup power response capability risk index and average priority weight of each type of load, compares the comprehensive risk value with a preset comprehensive risk threshold, and triggers an emergency rescheduling mode when the comprehensive risk value exceeds the comprehensive risk threshold; An adjustment module that dynamically adjusts the resource scheduling strategy of the integrated power supply system according to the risk type that contributes most to the grid access risk index, energy storage unit health risk index and backup power response capability risk index in the emergency rescheduling mode; An iteration module that continuously monitors the operating state of the integrated power supply system after dynamically adjusting the resource scheduling strategy of the integrated power supply system, and iteratively adjusts the resource scheduling strategy according to the change of the operating state until the comprehensive risk value falls below the comprehensive risk threshold.

Images