Intelligent scheduling method and system for optical-storage-diesel micro-grid

Abstract

The invention relates to the technical field of micro-grid dispatching, and particularly provides an intelligent dispatching method and system for a photovoltaic-storage-diesel micro-grid, and the method comprises the steps: obtaining the operation data of an energy storage battery, the operation data of a diesel generator, and the global operation data of the micro-grid; evaluating the power response capability of the energy storage battery according to the operation data of the energy storage battery, and evaluating the quick response capability of the diesel generator according to the operation data of the diesel generator; analyzing the stability risk level of the micro-grid according to the global operation data, the power response capability and the quick response capability of the micro-grid; adjusting scheduling optimization target emphasis according to the stability risk level; emphatically acquiring an energy storage battery charging and discharging instruction, a diesel generator starting and stopping instruction and an output instruction according to the adjusted scheduling optimization target, and executing all the acquired instructions; according to the method, the problem that the system stability is sacrificed due to blind pursuit of cost minimization in a traditional strategy can be avoided.

Description

Technical Field

[0001] This application relates to the field of microgrid dispatching technology, and more specifically, to a smart dispatching method and system for a photovoltaic-storage-diesel microgrid. Background Technology

[0002] In photovoltaic-storage-diesel microgrid systems, in order to minimize operating costs, dispatch systems often prioritize and extensively utilize energy storage batteries to smooth power fluctuations and meet load demands. However, this strategy gradually reveals its limitations during long-term operation, especially when facing high-frequency, small-amplitude charge-discharge cycles and sudden, large-scale load surges. The instantaneous power response capability of energy storage batteries degrades due to accumulated internal losses, while diesel generators, acting as backup power, have inherent start-up and power ramp-up delays. This makes it difficult for the microgrid to maintain stable voltage and frequency at critical moments, thereby affecting the normal operation of sensitive equipment and accelerating further battery aging.

[0003] Specifically, under a dispatch strategy centered on minimizing fuel costs, the system tends to maximize the utilization of photovoltaic power generation and energy storage batteries (used to store the electricity generated by photovoltaic modules), keeping diesel generators offline or running only briefly. Energy storage batteries undertake a significant amount of power regulation, frequently undergoing shallow charge-discharge cycles, leading to an increase in their internal impedance. During high-rate charge-discharge or instantaneous high-power output, the voltage drop at the terminals increases, reducing their ability to maintain stable output. When the microgrid load experiences a large step change in a very short time, although the energy storage battery's state of charge may be sufficient, the increased internal impedance causes a more drastic drop in terminal voltage than expected when providing a sudden surge in peak power. Simultaneously, diesel generators have inherent start-up and power ramp-up delays, making it impossible for them to immediately intervene and provide sufficient support during the short window of limited dynamic response of the energy storage batteries.

[0004] This phenomenon leads to frequent, brief voltage dips and frequency fluctuations in microgrid systems under instantaneous high-load step surges. The amplitude of these fluctuations is sufficient to trigger protection mechanisms in sensitive industrial equipment, causing unexpected shutdowns or operational interruptions. Furthermore, under these extreme conditions, energy storage battery systems are frequently pushed to their dynamic performance limits, further accelerating their irreversible performance degradation. This dispatch strategy, centered on minimizing fuel costs, fails to adequately consider the dynamic health status of energy storage batteries and their impact on system instantaneous stability, as well as their coordination with diesel generator response speeds, under long-term operation and complex dynamic load conditions, thus increasing the stability risks of the microgrid.

[0005] Currently, there is no effective technical solution to the above-mentioned problems. It should be noted that the information disclosed in this section is only for understanding the background of the present invention and therefore may include information that does not constitute prior art. Summary of the Invention

[0006] The purpose of this application is to provide a smart scheduling method and system for photovoltaic-storage-diesel microgrids, which can avoid the problem of sacrificing system stability due to blindly pursuing cost minimization in traditional strategies.

[0007] In a first aspect, this application provides a smart dispatching method for a photovoltaic-storage-diesel microgrid, which includes the following steps:

[0008] S1. Acquire energy storage battery operation data, diesel generator operation data, and microgrid global operation data;

[0009] S2. Evaluate the power response capability of the energy storage battery based on the energy storage battery operation data, and evaluate the rapid response capability of the diesel generator based on the diesel generator operation data.

[0010] S3. Analyze the stability risk level of the microgrid based on the microgrid's global operation data, power response capability, and rapid response capability;

[0011] S4. Adjust the focus of scheduling optimization objectives based on the stability risk level;

[0012] S5. Based on the adjusted scheduling optimization target, focus on obtaining the energy storage battery charging and discharging instructions, as well as the diesel generator start / stop instructions and output instructions, and execute all the obtained instructions.

[0013] Secondly, this application also provides a smart dispatching system for a photovoltaic-storage-diesel microgrid, comprising:

[0014] The data acquisition module is used to acquire operating data of energy storage batteries, operating data of diesel generators, and global operating data of the microgrid.

[0015] The capability assessment module is used to assess the power response capability of the energy storage battery based on the energy storage battery's operating data, and to assess the rapid response capability of the diesel generator based on the diesel generator's operating data.

[0016] The risk assessment module is used to analyze the stability risk level of the microgrid based on the microgrid's global operation data, power response capability, and rapid response capability.

[0017] The emphasis adjustment module is used to adjust the emphasis of scheduling optimization objectives based on the stability risk level.

[0018] The execution module is used to obtain energy storage battery charging and discharging instructions, diesel generator start / stop instructions, and output instructions based on the adjusted scheduling optimization objectives, and execute all the obtained instructions.

[0019] As can be seen from the above, the intelligent dispatching method and system for photovoltaic-storage-diesel microgrids provided in this application can first analyze the stability risk level of the microgrid based on the global operation data, power response capability, and rapid response capability of the microgrid, and then adjust the focus of dispatching optimization objectives according to the stability risk level. This allows the dispatching system to flexibly balance economic and reliability objectives based on the real-time health status and potential risks of the microgrid, thus avoiding the problem of sacrificing system stability due to blindly pursuing cost minimization in traditional strategies. Therefore, this application can significantly improve the overall operational reliability of the microgrid, extend the service life of energy storage batteries, and reduce economic losses caused by system instability. Attached Figure Description

[0020] Figure 1 A flowchart of a smart scheduling method for a photovoltaic-storage-diesel microgrid provided in this application embodiment.

[0021] Figure 2 This is a schematic diagram of a smart scheduling method for a photovoltaic-storage-diesel microgrid provided in an embodiment of this application.

[0022] Figure 3 This is a schematic diagram of the structure of a photovoltaic-storage-diesel microgrid intelligent dispatching system provided in an embodiment of this application.

[0023] Attached diagram labels: 1. Data acquisition module; 2. Capability assessment module; 3. Risk assessment module; 4. Focus adjustment module; 5. Execution module. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0025] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] Firstly, such as Figure 1 and Figure 2As shown, this application provides a smart dispatching method for a photovoltaic-storage-diesel microgrid, which includes the following steps:

[0027] S1. Acquire energy storage battery operation data, diesel generator operation data, and microgrid global operation data;

[0028] S2. Evaluate the power response capability of the energy storage battery based on the energy storage battery operation data, and evaluate the rapid response capability of the diesel generator based on the diesel generator operation data.

[0029] S3. Analyze the stability risk level of the microgrid based on the microgrid's global operation data, power response capability, and rapid response capability;

[0030] S4. Adjust the focus of scheduling optimization objectives based on the stability risk level;

[0031] S5. Based on the adjusted scheduling optimization target, focus on obtaining the energy storage battery charging and discharging instructions, as well as the diesel generator start / stop instructions and output instructions, and execute all the obtained instructions.

[0032] In this embodiment, the energy storage battery operating data refers to various parameters reflecting the current operating status and performance of the energy storage battery (such as the battery's terminal voltage and charging / discharging current). This data forms the basis for assessing the battery's health and dynamic response capabilities. The energy storage battery is used to store the electricity generated by the photovoltaic power generation modules. Similarly, the diesel generator operating data refers to various parameters reflecting the diesel generator's operating status and performance (such as the generator speed and fuel pressure). This data is used to assess the diesel generator's start-up speed and power ramp-up capability. The microgrid global operating data refers to various parameters reflecting the overall operating status of the microgrid (such as bus voltage and bus frequency). This data is crucial for analyzing microgrid stability risks. The power response capability in this embodiment refers to the energy storage battery's ability to quickly and accurately adjust its output power to meet system demands after receiving a power command. The rapid response capability in this embodiment refers to the diesel generator's ability to quickly start and reach the required output power after receiving a start-up or output command. The stability risk level in this embodiment refers to a quantitative assessment of the probability and severity of voltage or frequency instability events occurring in the microgrid under its current operating state. The scheduling optimization objective of this embodiment focuses on assigning different weights or priorities to different optimization objectives such as economy, reliability, and environmental protection during the microgrid scheduling process, based on actual operational needs and risk assessment results.

[0033] In step S1, it is necessary to acquire the operating data of the energy storage battery, the operating data of the diesel generator, and the global operating data of the microgrid. These data are the basis for subsequent evaluation and analysis. Specifically, this embodiment can acquire the operating data of the energy storage battery by setting sensors (such as voltage sensors, current sensors, and temperature sensors) in the energy storage battery management system (BMS) and acquiring real-time data based on these sensors. This embodiment can acquire the operating data of the diesel generator by setting sensors (such as speed sensors, temperature sensors, and pressure sensors) in the diesel generator controller (GAC) and acquiring real-time data based on these sensors. This embodiment can acquire the global operating data of the microgrid by installing smart meters, voltage transformers (PTs), and current transformers (CTs) at key nodes (such as busbars) of the microgrid for real-time data monitoring. This global operating data of the microgrid can reflect the current load status of the microgrid.

[0034] In step S2, the power response capability of the energy storage battery is evaluated based on the acquired operating data, and the rapid response capability of the diesel generator is evaluated based on the operating data. This embodiment can evaluate the power response capability of the energy storage battery based on parameters such as its state of charge (SOC), internal temperature, terminal voltage, and charge / discharge current. Specifically, this embodiment can evaluate the power response capability by consulting preset battery performance curves or by using a battery equivalent circuit model for calculation. For example, based on the battery's current SOC and temperature, the corresponding maximum charge / discharge power limit can be found in the manufacturer's performance data sheet, and the power response capability can be estimated by combining the battery's internal resistance change trend. This embodiment can also evaluate the rapid response capability of the diesel generator based on parameters such as generator speed, coolant temperature, fuel pressure, and operating mode, combined with its historical start-up time, power ramp-up rate, and other performance indicators. For example, based on the diesel generator's current operating mode (such as shutdown, idling, or grid-connected operation), combined with its preset start-up delay time curve and rated power ramp-up rate, the time required for it to reach the target power after receiving a command can be predicted.

[0035] In step S3, the stability risk level of the microgrid is analyzed based on the global operating data of the microgrid, the power response capability of the energy storage battery, and the rapid response capability of the diesel generator. Specifically, this embodiment can analyze the stability risk level of the microgrid by first inputting these data into a pre-established microgrid dynamic model to simulate the system response of the dispatch system under the current scenario, and then determining the risk level based on the system response. For example, the microgrid dynamic model can predict the bus voltage and frequency fluctuation amplitude of the dispatch system under the current global operating data of the microgrid based on the global operating data of the microgrid, the power response capability of the energy storage battery, and the rapid response capability of the diesel generator. Then, the stability risk level is determined based on the bus voltage and frequency fluctuation amplitude using a lookup table method. Preferably, the bus voltage and frequency fluctuation amplitude are positively correlated with the stability risk level of the microgrid.

[0036] In step S4, the focus of the scheduling optimization objective is adjusted according to the stability risk level. For example, when the microgrid's stability risk level is low, the scheduling optimization objective can focus on minimizing operating costs, i.e., prioritizing the use of photovoltaic power generation and energy storage batteries to reduce the operating time of diesel generators. When the stability risk level is high, the scheduling optimization objective can focus on system reliability, i.e., increasing the reserve capacity of diesel generators or starting diesel generators in advance to ensure that power support can be provided quickly in the event of an emergency, even if this may lead to an increase in fuel costs.

[0037] In step S5, the system acquires the energy storage battery charging / discharging commands, as well as the diesel generator start / stop and output commands, based on the adjusted scheduling optimization objective, and executes all acquired commands. Specifically, the scheduling system runs optimization algorithms (such as model predictive control, linear programming, or heuristic algorithms) to generate optimal energy storage battery charging / discharging commands (including charging / discharging power and duration), as well as diesel generator start / stop and output commands, based on the adjusted optimization objective. These commands are then sent to the energy storage battery management system and the diesel generator controller to achieve intelligent scheduling of the microgrid.

[0038] The intelligent dispatching method for photovoltaic-storage-diesel microgrids proposed in this application achieves adaptive adjustment of dispatching optimization objectives by introducing an assessment of the power response capability of energy storage batteries and the rapid response capability of diesel generators, and combining this with dynamic analysis of stability risk levels based on global microgrid operation data. This method effectively solves the problems of decreased system stability and shortened battery life caused by over-reliance on energy storage batteries in traditional dispatching strategies.

[0039] Specifically, traditional dispatch strategies often prioritize minimizing fuel costs, leading to frequent shallow charge-discharge cycles of energy storage batteries. This increases the cumulative internal impedance, resulting in greater voltage drops during high-rate charging / discharging or instantaneous high-power output, thus reducing the ability to maintain stable output. When the microgrid load experiences a significant step change within a very short period, even if the energy storage battery's state of charge is sufficient, the cumulative increase in its internal impedance causes a more severe drop in terminal voltage than expected when providing a sudden surge in peak power. Simultaneously, diesel generators have inherent start-up and power ramp-up delays, preventing them from immediately intervening to provide sufficient support within the limited window of dynamic response of the energy storage battery. This phenomenon leads to frequent brief voltage drops and frequency fluctuations in the microgrid system under instantaneous high-load step changes, with amplitudes sufficient to trigger protection mechanisms in sensitive industrial equipment, causing unexpected shutdowns or operational interruptions. Furthermore, the energy storage battery system is frequently pushed to its dynamic performance limits under these extreme conditions, further accelerating its irreversible performance degradation.

[0040] This application first acquires operational data from energy storage batteries, diesel generators, and the entire microgrid before making dispatch decisions. Based on this data, it assesses the power response capability of the energy storage batteries and the rapid response capability of the diesel generators. Subsequently, based on these capability assessments and the global microgrid operational data, it analyzes the stability risk level of the microgrid. When a high stability risk is identified, the focus of dispatch optimization shifts from simple economics to higher reliability. For example, it may increase the reserve capacity of diesel generators or start them in advance to ensure rapid power support in the event of an emergency. This dynamic adjustment mechanism allows the dispatch system to flexibly balance economic and reliability objectives based on the real-time health status and potential risks of the microgrid. This avoids the problem of sacrificing system stability in the blind pursuit of cost minimization found in traditional strategies. Therefore, this application can significantly improve the overall operational reliability of the microgrid, extend the lifespan of energy storage batteries, and reduce economic losses caused by system instability.

[0041] In some preferred embodiments, the energy storage battery operating data includes the battery's terminal voltage, charge / discharge current, state of charge, internal temperature, and output power; the diesel generator operating data includes the diesel generator's generator speed, coolant temperature, fuel pressure, and operating mode; and the microgrid global operating data includes the bus voltage, bus frequency, and total load demand.

[0042] The terminal voltage and charge / discharge current of an energy storage battery are key parameters for real-time monitoring of its electrical characteristics. These parameters reflect the battery's instantaneous state of charge and discharge. The state of charge (SOC) of the energy storage battery indicates its remaining energy, which is crucial for energy management and scheduling decisions. The internal temperature of the energy storage battery is a key parameter for assessing its health, safe operating boundaries, and thermal management requirements. The output power of the energy storage battery directly reflects its energy throughput capacity at a specific moment, and this output power is essential for evaluating its power response capability. Generator speed and fuel pressure are important indicators for assessing the operating condition, fuel consumption efficiency, and mechanical health of a diesel generator. Coolant temperature reflects the engine's thermal load and the operating status of the cooling system. The operating mode of the diesel generator indicates its current working state, such as standby, startup, grid-connected operation, or shutdown, which is instructive for evaluating its rapid response capability and formulating scheduling strategies. Bus voltage and bus frequency are core indicators of power quality and system stability in microgrids. Fluctuations in bus voltage and frequency directly reflect the operational health of the microgrid. Total load demand reflects the real-time power consumption of all loads within the microgrid, serving as fundamental data for power source scheduling and supply-demand balancing. Through the aforementioned technical solution, this embodiment acquires more comprehensive and refined operational data from energy storage batteries, diesel generators, and the entire microgrid, enabling more accurate performance evaluations of various devices and a deeper understanding of the microgrid's operational status. Therefore, this embodiment effectively improves the accuracy of power response and rapid response capability assessments, thereby enhancing the reliability of microgrid stability risk level analysis. This provides stronger data support for subsequent scheduling optimization adjustments and command execution, ultimately optimizing the overall operational efficiency and stability of the microgrid.

[0043] The above implementation proposes to evaluate the power response capability of energy storage batteries based on their operating data. However, in practice, when the sensors used to evaluate the instantaneous power support capability of the energy storage batteries experience measurement deviations during rapid transient responses due to environmental dust accumulation, the evaluation results may not match the actual dynamic performance of the batteries, thereby affecting the accuracy and reliability of microgrid dispatch.

[0044] To address this technical problem, in some preferred embodiments, step S2 includes:

[0045] S21. Based on the operating data of the energy storage battery, make a preliminary assessment of the power response capability of the energy storage battery to obtain preliminary assessment results;

[0046] S22. Evaluate the rapid response capability of the diesel generator based on its operating data;

[0047] S23. Based on the energy storage battery operation data, diesel generator operation data and microgrid global operation data during the load impact event, obtain the bus voltage fluctuation amplitude, bus frequency fluctuation amplitude, actual output of diesel generator and actual output power of battery.

[0048] S24. Obtain the instantaneous output power of the energy storage battery based on the bus voltage fluctuation amplitude, bus frequency fluctuation amplitude, actual output of the diesel generator and the electrical characteristics of the microgrid.

[0049] S25. Based on the comparison between the instantaneous output power and the actual output power of the battery, revise the preliminary evaluation results to obtain the power response capability of the energy storage battery.

[0050] In step S21, the preliminary assessment of the power response capability of the energy storage battery can be based on the battery's historical operating data, performance parameters provided by the manufacturer, and a preset degradation model. For example, based on the battery's state of charge, internal temperature, and other operating data, combined with its design life curve and cycle count, the maximum charge / discharge power and response speed under the current condition can be preliminarily determined, thus obtaining a preliminary assessment result. In step S22, the assessment of the diesel generator's rapid response capability can be understood as an assessment of the time required for the diesel generator to reach its rated output from startup and its power ramp-up rate. This assessment can be dynamically adjusted based on the diesel generator's historical operating data to more accurately reflect its response performance in actual operation. The preferred conditions for judging the load impact event in step S23 are: the ratio of the rate of change of total load demand to the rated load of the microgrid exceeds the first preset threshold (e.g., 15%), the ratio of the rate of change of bus voltage to the rated voltage of the microgrid exceeds the second preset threshold (e.g., 5%), or the ratio of the rate of change of bus frequency to the rated frequency of the microgrid exceeds the third preset threshold (e.g., 5%). Step S23 is equivalent to accurately capturing the key electrical parameters of the microgrid and the actual response of each power source when the impact event occurs, based on the energy storage battery operation data, diesel generator operation data and microgrid global operation data at the time of the load impact event. The microgrid electrical characteristics in step S24 can include information such as the microgrid's equivalent impedance, topology, line parameters, and the connection point of distributed power sources. Step S24 can utilize power system theory (e.g., based on power balance equations or transient stability analysis models) to deduce the instantaneous output power actually provided by the energy storage battery during load surge events, based on the bus voltage fluctuation amplitude, bus frequency fluctuation amplitude, and actual diesel generator output, combined with the microgrid electrical characteristics. Step S24 is equivalent to verifying or calculating the instantaneous output power of the energy storage battery through the overall response of the microgrid, regardless of whether there are deviations in the energy storage battery's own power sensor. The comparison result in step S25 refers to comparing the instantaneous output power of the energy storage battery derived in step S24 with the actual output power of the battery directly obtained in step S23. If there is a difference between the two, it indicates that the preliminary evaluation result may be biased. At this time, the preliminary evaluation result can be corrected based on the difference, for example, by introducing a correction factor or adjustment amount, so that the corrected power response capability is more consistent with the true performance of the energy storage battery in actual transient response.

[0051] This application effectively avoids the impact of single sensor measurement deviations on the evaluation results by introducing an indirect derivation and comparison correction mechanism between the instantaneous output power of the energy storage battery and the actual output power. Because after initially evaluating the energy storage battery's power response capability, the instantaneous output power of the energy storage battery is derived by acquiring the overall response data of the microgrid during load impact events (such as bus voltage fluctuation amplitude and bus frequency fluctuation amplitude), and combining this with the actual output of the diesel generator and the electrical characteristics of the microgrid, even if the energy storage battery's own power sensor has a deviation, its instantaneous output power can still be indirectly verified or estimated through the overall response of the microgrid. Subsequently, the derived instantaneous output power is compared with the battery's actual output power, and the comparison result is used to correct the initial evaluation results. This correction mechanism can compensate for the inaccuracy of the evaluation caused by sensor measurement deviations, ensuring that the final energy storage battery power response capability is closer to its true dynamic performance. Through the above technical solution, this application can effectively solve the problem that when the sensor used to evaluate the instantaneous power support capability of the energy storage battery has measurement deviations due to factors such as environmental dust accumulation, the evaluation results do not match the actual dynamic performance of the battery. By introducing a comparison and correction mechanism between the instantaneous output power derivation based on the overall response of the microgrid and the actual output power, the accuracy and reliability of the power response capability assessment of the energy storage battery are significantly improved. Therefore, this embodiment can provide more accurate input for the stability risk analysis and scheduling optimization of the microgrid, thereby improving the overall performance and economy of the intelligent scheduling of the microgrid and avoiding misjudgments and unnecessary system fluctuations caused by assessment bias.

[0052] In some preferred embodiments, it is assumed that after a period of operation, the instantaneous power sensor of the energy storage battery in a photovoltaic-storage-diesel microgrid experiences a 0.5% measurement deviation during rapid charge-discharge transients due to dust accumulation in the environment. According to traditional evaluation methods, this deviation will directly cause the evaluation result of the energy storage battery's power response capability to deviate from the actual situation. However, in the scheme of this application, a preliminary evaluation is first performed based on the historical operating data of the energy storage battery and manufacturer parameters to obtain a preliminary evaluation result. When a sudden load impact event occurs in the microgrid, the system obtains the bus voltage fluctuation amplitude, bus frequency fluctuation amplitude, actual output of the diesel generator, and actual output power of the battery based on the energy storage battery operating data, diesel generator operating data, and global microgrid operating data at the time of the event. Simultaneously, based on the bus voltage fluctuation amplitude, bus frequency fluctuation amplitude, actual output of the diesel generator, and actual output power of the battery, combined with the electrical characteristics of the microgrid, the instantaneous output power of the energy storage battery during the impact event is calculated by reverse derivation. If there is a significant difference between the derived instantaneous output power and the actual battery output power (e.g., the derived value is 0.5% higher than the actual value), it indicates that the preliminary evaluation results may be biased. In this case, the system will correct the preliminary evaluation results based on this comparison, for example, by lowering the preliminary power response capability by 0.5%, thereby obtaining a more accurate power response capability for the energy storage battery. In this way, even if there are measurement biases in the sensors, the solution proposed in this application can effectively correct the evaluation results, ensuring the accuracy of scheduling decisions.

[0053] The above implementation method proposes to evaluate the rapid response capability of diesel generators based on their operating data. However, the actual rapid response capability of a diesel generator is not only affected by its current operating status data, but also closely related to its environmental conditions (such as temperature, humidity, and altitude), as well as its long-term historical operating performance and maintenance status. If the evaluation is based solely on current operating data, it may not fully and accurately reflect the true dynamic response performance of the diesel generator under different operating conditions, which could lead to biases in the judgment of the microgrid stability risk level, thereby affecting the effectiveness and reliability of the dispatching strategy.

[0054] To address this technical problem, in some preferred embodiments, step S22 includes:

[0055] S221. Obtain the ambient temperature, humidity and altitude of the diesel generator location, and obtain the historical operating data and maintenance records of the diesel generator;

[0056] S222. Adjust the preset start-up time characteristic curve and power ramp-up rate characteristic curve according to ambient temperature, ambient humidity, ambient altitude, historical operating data and maintenance records;

[0057] S223. Evaluate the rapid response capability of the diesel generator based on the adjusted start-up time characteristic curve, power ramp-up rate characteristic curve, and diesel generator operating data.

[0058] In step S221, the ambient temperature, humidity, and altitude of the diesel generator's location can be monitored and acquired in real time by sensors deployed near the generator. These environmental parameters significantly affect the generator's combustion efficiency, cooling system performance, and air density, thus directly impacting its start-up time and power output capability. Historical operating data of the diesel generator can include its past start-up success rate, power ramp-up time under different loads, fault records, operating hours, and fuel consumption rate. This data reflects the long-term performance trends and potential problems of the diesel generator. Maintenance records can include regular maintenance details, component replacement records, and repair history. This information is crucial for assessing the current mechanical condition of the diesel generator and predicting its future response capabilities. In step S222, the preset start-up time characteristic curve and power ramp-up rate characteristic curve are baseline performance curves established based on the diesel generator's factory parameters or standard test conditions. Step S222 refines these preset curves based on ambient temperature, humidity, altitude, historical operating data, and maintenance records to ensure that the adjusted start-up time characteristic curve and power ramp-up rate characteristic curve more accurately reflect the diesel generator's current actual performance. In step S223, after obtaining the adjusted start-up time characteristic curve and power ramp-up rate characteristic curve, the rapid response capability of the diesel generator can be evaluated by combining the current diesel generator operating data (e.g., generator speed, coolant temperature, fuel pressure, and operating mode). Since the evaluation process of the diesel generator's rapid response capability no longer relies solely on static operating data but takes into account dynamic environmental influences and historical performance degradation, this embodiment can obtain a more realistic and reliable rapid response capability evaluation result.

[0059] This application's solution addresses the discrepancy between traditional methods' assessment results and the actual dynamic performance of diesel generators by incorporating environmental factors, historical operating data, and maintenance records when evaluating the rapid response capability of diesel generators, and dynamically adjusting preset performance characteristic curves accordingly. Specifically, ambient temperature, humidity, and altitude directly affect the physical operating conditions of diesel generators; for example, air density affects intake volume and combustion efficiency, while low temperatures affect lubricating oil viscosity and battery starting performance. Historical operating data and maintenance records provide a long-term "health record" of the diesel generator, revealing performance degradation trends and potential failure risks. By integrating this multi-dimensional information and using it to correct the start-up time characteristic curve and power ramp-up rate characteristic curve, these curves more accurately reflect the dynamic response limits of the diesel generator under current actual operating conditions. Therefore, assessments based on the adjusted characteristic curves and real-time operating data can more accurately predict the actual performance of the diesel generator when rapid response is required, avoiding scheduling decision errors caused by assessment bias. Because this embodiment considers the combined impact of environmental conditions, historical operating data, and maintenance records on the performance of diesel generators when evaluating their rapid response capability, it enables the obtained rapid response capability assessment results to more realistically reflect the actual dynamic response capability of diesel generators under different operating conditions. In other words, this embodiment effectively improves the accuracy and reliability of the rapid response capability assessment of diesel generators. This helps the microgrid intelligent dispatching system to more accurately determine the stability risk level of the microgrid, thereby formulating more reasonable and reliable dispatch optimization objectives and specific dispatch instructions. This effectively improves the economic efficiency of microgrid operation, enhances its ability to cope with sudden load changes or renewable energy fluctuations, and ensures the stable operation of the microgrid.

[0060] In some preferred embodiments, a specific example is given below. Assume a diesel generator in a photovoltaic-storage-diesel microgrid is located in a high-altitude region at 2000 meters above sea level, where winter temperatures are often below -10°C and summer temperatures can reach 30°C, and the generator has been operating for five years. In step S221, the system obtains real-time data showing a current ambient temperature of -5°C, humidity of 60%, and altitude of 2000 meters. Simultaneously, the system retrieves historical operating data from the database for the diesel generator over the past five years, including data on lower winter start-up success rates, an average 15% increase in start-up time, and a slight decrease in summer power ramp-up rate. Maintenance records show that the fuel filter was replaced three years ago, but no major overhaul has been performed in the past year. In step S222, based on these obtained environmental parameters, historical operating data, and maintenance records, the system adjusts the preset start-up time characteristic curve and power ramp-up rate characteristic curve. For example, due to the low ambient temperature and high altitude, the system will shift the start-up time characteristic curve upwards according to a preset correction model to reflect the adverse effects of low temperature and high altitude on start-up performance. Simultaneously, considering the decreasing start-up success rate and slightly decreasing power ramp-up rate shown in historical operating data, the system will further fine-tune these curves to more closely reflect the actual performance of the specific diesel generator under current conditions. In step S223, using the adjusted start-up time characteristic curve and power ramp-up rate characteristic curve, combined with the current diesel generator operating data (e.g., current generator speed is 1500 rpm, coolant temperature is 70°C, fuel pressure is normal, and operating mode is standby), the system evaluates the rapid response capability of the diesel generator under the current environment and health condition. For example, the evaluation result may show that in an emergency, the start-up time from standby to full load output is 15 seconds, and the power ramp-up rate is 50 kW / s, which is significantly different from the results evaluated only based on factory parameters or standard environmental conditions (e.g., 10-second start-up, 70 kW / s ramp-up). In this way, the dispatching system can obtain more accurate information on the rapid response capability of diesel generators, thereby enabling it to allocate the response tasks of energy storage batteries and diesel generators more rationally when the microgrid faces load shocks, and avoiding the risk of system instability caused by overestimating the response capability of diesel generators.

[0061] The above-described implementation method requires obtaining the instantaneous output power of the energy storage battery based on factors such as bus voltage fluctuations, bus frequency fluctuations, actual diesel generator output, and microgrid electrical characteristics when assessing the battery's power response capability. However, in actual operation, the equivalent impedance or topology of the microgrid may change rapidly due to switching operations, line faults, or dynamic changes in distributed loads. If the microgrid electrical characteristics used to derive the instantaneous output power of the energy storage battery fail to update in real time or accurately reflect these dynamic changes, the power back-calculation results based on bus voltage and frequency fluctuations will be biased, thus affecting the accuracy of the energy storage battery power response capability assessment.

[0062] To address this technical problem, in some preferred embodiments, step S24 includes:

[0063] S241. Obtain the switching status and distributed load operating status in the microgrid when a load surge event occurs;

[0064] S242. Determine the electrical characteristics of the microgrid based on the switch status and the operating status of the distributed load;

[0065] S243. Obtain the instantaneous output power of the energy storage battery based on the bus voltage fluctuation amplitude, bus frequency fluctuation amplitude, actual output of the diesel generator, and electrical characteristics of the microgrid.

[0066] The switching states in step S241 refer to the open / closed states of various circuit breakers, disconnectors, load switches, and other equipment in the microgrid. These states directly determine the microgrid's topology. The distributed load operating states in step S241 refer to the connection and disconnection of various distributed loads (such as smart home devices or industrial loads) connected to the microgrid, as well as changes in their own power demands. Step S241 is equivalent to capturing the actual physical connections and load distribution of the microgrid when a load surge event occurs, providing basic data for the subsequent accurate derivation of the microgrid's electrical characteristics. The microgrid electrical characteristics in step S242 refer to the equivalent impedance, network topology, line parameters, and other electrical parameters of the microgrid under specific operating conditions. Step S242 can construct an equivalent circuit model of the microgrid in real time based on the obtained switch state using network topology analysis algorithms (e.g., node admittance matrix method, branch impedance matrix method, etc.). At the same time, the load parameters in the equivalent circuit model are dynamically updated in combination with the operating status of distributed loads. Step S242 is equivalent to enabling the electrical characteristics of the microgrid to accurately reflect the actual operating status of the microgrid when a load impact event occurs, so as to avoid evaluation deviations caused by static or outdated models.

[0067] This solution captures the dynamic changes in the microgrid's topology and load distribution in real time by obtaining the switching states and distributed load operating states during load surge events. It is precisely because of this real-time data acquisition that the microgrid's electrical characteristics (such as equivalent impedance and network topology) can be accurately determined based on these dynamically changing switching states and distributed load operating states. In this way, the microgrid's electrical characteristics are no longer static preset values, but rather reflect the actual operating status of the microgrid in real time. Based on this, and combining real-time updated microgrid electrical characteristics with bus voltage fluctuation amplitude, bus frequency fluctuation amplitude, and actual diesel generator output, the instantaneous output power of the energy storage battery can be more accurately derived in reverse. This effectively avoids the power back-calculation deviation caused by inaccurate electrical characteristics due to dynamic changes in the microgrid. Compared with traditional methods that fail to update or accurately reflect the dynamic changes in microgrid electrical characteristics in real time, this scheme effectively solves the problem of inaccurate microgrid electrical characteristics caused by rapid changes in the microgrid's equivalent impedance or topology. Therefore, this scheme can make the derived instantaneous output power of the energy storage battery closer to the actual value. In other words, this application can significantly improve the accuracy of energy storage battery power response capability assessment, providing a more reliable data foundation for subsequent microgrid stability risk level analysis and scheduling optimization.

[0068] In some preferred embodiments, a specific example is given below. Suppose that in a photovoltaic-storage-diesel microgrid, when a large industrial load (e.g., a high-power motor) is suddenly connected or disconnected, it causes instantaneous fluctuations in the microgrid bus voltage and frequency, which is considered a load surge event. To accurately assess the instantaneous power support capability of the energy storage battery in this event, the scheduling method of this application immediately executes step S241, acquiring the real-time status of all circuit breakers and disconnectors in the microgrid through a SCADA system (Supervisory and Data Acquisition System), and acquiring the operating status and power changes of the large industrial load and other distributed loads (such as photovoltaic inverters, electric vehicle charging piles, etc.) from smart meters or load management systems. Subsequently, in step S242, based on these real-time acquired switch states and distributed load operating states, using a preset microgrid topology model and parameter library, the equivalent impedance matrix and node admittance matrix of the microgrid are dynamically calculated and updated using network analysis software (e.g., MATLAB's Simscape Electrical Toolbox), thereby determining the current microgrid electrical characteristics. Finally, in step S243, the real-time monitored bus voltage fluctuation amplitude, bus frequency fluctuation amplitude, and the actual output of the diesel generator during the impact event are input into the power balance equation or transient analysis model based on the updated microgrid electrical characteristics. This allows for the reverse calculation of the instantaneous output power of the energy storage battery during the load impact. For example, if the microgrid experiences a 5Hz frequency drop and a 0.1pu voltage drop after the impact, and the diesel generator output increases by 2MW, while the updated microgrid electrical characteristics calculate a total system power deficit of 3MW, then it can be accurately inferred that the energy storage battery provides 1MW of instantaneous power support. In this way, even if the microgrid topology or load undergoes drastic changes, the accuracy of the instantaneous output power assessment of the energy storage battery can be ensured.

[0069] In some preferred embodiments, step S25 includes:

[0070] S251. Obtain the instantaneous reactive power change rate and voltage-current phase angle change rate based on the microgrid global operation data when a load impact event occurs.

[0071] S252. The instantaneous output power is corrected based on the instantaneous reactive power change rate and the voltage and current phase angle change rate to reduce the impact of system response caused by non-energy storage battery performance factors on the instantaneous output power.

[0072] S253. Based on the comparison between the corrected instantaneous output power and the actual output power of the battery, revise the preliminary evaluation results to obtain the power response capability of the energy storage battery.

[0073] The instantaneous reactive power change rate in step S251 refers to the rate of change of reactive power in the microgrid within a very short time when a load surge event occurs. It reflects the system's rapid response or fluctuations in reactive power demand. The voltage-current phase angle change rate in step S251 refers to the rate of change of the phase angle difference between the microgrid bus voltage and current when a load surge event occurs. It can characterize the instantaneous changes in the system's impedance characteristics or load properties. These parameters can be obtained in real-time or near real-time from the microgrid's global operating data, for example, by sampling the bus voltage and bus current using high-precision sensors and combining them with power calculation algorithms. Step S252 corrects the instantaneous output power using the instantaneous reactive power change rate and the voltage-current phase angle change rate. Specifically, when dynamic load changes or equivalent impedance fluctuations occur in the microgrid, these non-energy storage battery performance factors often cause rapid changes in system reactive power and voltage-current phase angle. Therefore, this embodiment can quantify and separate the system response portion caused by non-battery factors in the instantaneous output power by using these change rates to correct the instantaneous output power, so that the instantaneous output power more accurately reflects the instantaneous active power contribution of the energy storage battery itself. Specifically, this embodiment can first use a lookup table method or machine learning model to determine the output power correction amount (which can be a value or a coefficient) based on these change rates, and then add or multiply the output power correction amount with the instantaneous output power to achieve the correction of the instantaneous output power based on the instantaneous reactive power change rate and the voltage-current phase angle change rate. Step S253 compares the corrected instantaneous output power with the actual output power of the battery to more accurately evaluate the power response capability of the energy storage battery and make a final correction to the preliminary evaluation results.

[0074] When inversely deriving the instantaneous output power of the energy storage battery from the overall response of the microgrid, in addition to the response of the energy storage battery, there may also be instantaneous changes in other dynamic loads or fluctuations in the equivalent impedance of the microgrid. These factors will jointly affect the fluctuations in bus voltage and frequency, resulting in the direct inverse calculation of the instantaneous output power of the energy storage battery including the system response caused by non-energy storage battery performance factors. This scheme introduces the instantaneous reactive power change rate and voltage-current phase angle change rate (these parameters effectively characterize the impact of non-energy storage battery factors (such as rapid switching of inductive or capacitive loads, changes in line impedance, etc.) on the transient response of the microgrid). By using these change rates to correct the initially derived instantaneous output power, this scheme effectively reduces or eliminates the system response caused by these non-energy storage battery performance factors. This allows the corrected instantaneous output power to more accurately reflect the true instantaneous active power contribution of the energy storage battery itself. In other words, this scheme effectively solves the problem of interference from non-energy storage battery performance factors on the assessment of the instantaneous output power of the energy storage battery in the overall response of the microgrid. Therefore, this scheme can effectively improve the accuracy of the instantaneous output power, making the assessment results of the energy storage battery power response capability more accurate and avoiding the decline in the economy and reliability of the dispatch strategy due to misjudgment. This helps the microgrid dispatch system to more accurately grasp the dynamic performance of the energy storage battery, thereby formulating a more optimized and reliable dispatch strategy and improving the overall operating efficiency and stability of the microgrid.

[0075] As a specific implementation method, a concrete example is given below. Suppose that in a photovoltaic-storage-diesel microgrid, when a large inductive load is suddenly connected, the microgrid bus voltage and frequency will experience instantaneous fluctuations. In traditional evaluation methods, the instantaneous output power of the energy storage battery might be directly inferred from these fluctuations. However, since the connection of this inductive load also causes a sharp change in the microgrid's instantaneous reactive power and a rapid shift in the voltage and current phase angles, the responses of these non-energy storage battery factors will be superimposed on the actual response of the energy storage battery, leading to an overestimation or underestimation of the inferred instantaneous output power. According to the scheme of this application, firstly, when a load impact event occurs, the instantaneous reactive power change rate and voltage and current phase angle change rate are obtained in real time through the microgrid's global operating data. For example, it can be monitored that the instantaneous reactive power rapidly increases from 0 MVar to 5 MVar, and the voltage and current phase angle changes from nearly 0 degrees to -30 degrees within milliseconds. Secondly, these change rates are used to correct the initially derived instantaneous output power of the energy storage battery. For example, a corrected model can be established to estimate the active power component caused by non-storage battery factors based on the reactive power change rate and phase angle change rate, and subtract it from the initially derived instantaneous output power. Assuming the initially derived instantaneous output power is 10MW, and the corrected model estimates that non-storage battery factors contribute 2MW, the corrected instantaneous output power is 8MW. Finally, this corrected 8MW is compared with the actual output power of the energy storage battery (e.g., 7.8MW measured by the BMS), thereby more accurately correcting the first preliminary assessment result and obtaining the power response capability of the energy storage battery. In this way, even in complex microgrid dynamic environments, the influence of non-storage battery factors can be effectively isolated, ensuring the accuracy of the assessment results for the energy storage battery's power response capability.

[0076] In some preferred embodiments, step S3 includes:

[0077] S31. Obtain the microgrid's operating mode, load characteristic parameters, and power configuration information;

[0078] S32. Determine the risk level assessment strategy based on the operating mode, load characteristic parameters, and power configuration information;

[0079] S33. Risk level judgment strategy: Analyze the stability risk level of the microgrid based on the microgrid's global operation data, power response capability, and rapid response capability.

[0080] The microgrid operation mode in step S31 can be understood as the current operating state of the microgrid, such as grid-connected mode, islanded mode, or hybrid mode. The load characteristic parameters in step S31 refer to the electrical characteristics and behavior patterns of the loads connected in the microgrid (such as the type of load (e.g., resistive, inductive, capacitive loads), load volatility, peak-valley characteristics, and sensitivity to voltage and frequency changes). The power configuration information in step S31 is the detailed information of all power sources in the microgrid (e.g., the rated capacity, actual output, technology type, connection point, and current operating status of each power source such as photovoltaic, energy storage batteries, and diesel generators). Step S32 is equivalent to dynamically determining a risk level judgment strategy suitable for the current microgrid state based on the microgrid's operating mode, load characteristic parameters, and power configuration information, so as to make the stability risk assessment more targeted and effective. For example, when the microgrid is in islanded mode and the load fluctuation is large, the system may choose a judgment strategy with stricter requirements for frequency and voltage stability; while when the microgrid is in grid-connected mode and the power redundancy is high, a judgment strategy that focuses more on economy or power quality may be adopted.

[0081] This solution first comprehensively understands the current operating environment and inherent vulnerabilities of the microgrid by acquiring its operating mode, load characteristic parameters, and power configuration information in step S31. Then, based on this data, it dynamically selects or generates a set of risk assessment rules or models (risk level judgment strategies) best suited to the current state. This makes the stability risk level assessment more accurate and targeted, avoiding misjudgments or underestimations caused by using a single or general assessment standard. Finally, using this customized judgment strategy, combined with the microgrid's real-time operating data, the power response capability of the energy storage battery, and the rapid response capability of the diesel generator, a more refined and accurate stability risk analysis is conducted. This shift from general assessment to contextualized assessment ensures that the stability risk level analysis results truly reflect the stability status of the microgrid under specific operating conditions, thus providing a more solid and reliable foundation for subsequent dispatch decisions, and ultimately improving the adaptability, reliability, and economy of the entire photovoltaic-storage-diesel microgrid intelligent dispatch method.

[0082] In some preferred embodiments, a specific example is given below. Assume a photovoltaic-storage-diesel microgrid designed to switch between grid-connected and islanded modes, connected to critical loads with high power quality requirements (e.g., hospitals) and highly volatile renewable energy sources (e.g., photovoltaics). Specifically, in step S31, the system first obtains that the microgrid is currently in "islanded mode," its load characteristic parameters indicate the presence of "critical sensitive loads," and the power configuration information indicates that "photovoltaic output accounts for a high proportion." Subsequently, in step S32, based on this information, the system determines a risk level assessment strategy for "high-priority stability assurance." This strategy may include: setting stricter thresholds for frequency and voltage fluctuation ranges; imposing higher requirements on the power response capability of the energy storage battery and the rapid response capability of the diesel generator; and assigning higher weight to frequency and voltage stability during evaluation. Finally, in step S33, based on the aforementioned "high-priority stability guarantee" strategy, the system comprehensively analyzes the real-time acquired global microgrid operation data (e.g., a slight decrease in bus frequency caused by a sudden drop in photovoltaic output due to cloud cover), the power response capability of the energy storage battery, and the rapid response capability of the diesel generator. If the analysis results show that, under the current load and power configuration, the response capability of the energy storage battery and the diesel generator is insufficient to control frequency fluctuations within the strict threshold set by the "high-priority stability guarantee" strategy, then the microgrid's stability risk level will be judged as "high risk." This precise and contextualized risk assessment result will directly guide the subsequent adjustment of scheduling optimization objectives, such as prioritizing the stable operation of the system, even if it means sacrificing some economic efficiency.

[0083] In some preferred embodiments, step S4 includes:

[0084] S41. Adjust the focus of the scheduling optimization objective according to the stability risk level to obtain the initial focus of the optimization objective;

[0085] S42. Obtain the life degradation characteristics of the energy storage battery;

[0086] S43. Evaluate the impact of the initial optimization objective on the full life cycle operating cost of the energy storage battery based on the initial optimization objective focus and life decay characteristics, so as to obtain the impact assessment results;

[0087] S44. Based on the impact assessment results, the initial optimization objective is revised to obtain the final scheduling optimization objective.

[0088] The initial optimization objective in step S41 can be understood as the scheduling objective obtained after prioritizing or weighting the scheduling objectives (e.g., minimizing operating costs, maximizing renewable energy absorption, maintaining system frequency and voltage stability) while only considering the microgrid stability risk level. In step S42, obtaining the lifespan degradation characteristics of the energy storage battery refers to acquiring data or models describing the performance degradation laws of the energy storage battery under different operating conditions (e.g., depth of charge / discharge, charge / discharge rate, temperature, etc.), such as capacity decay and internal resistance increase. These lifespan degradation characteristics may include the battery's cycle life curve, calendar life model, and the influence factor of temperature on degradation. Step S43 can employ a lookup table method, simulation analysis based on mathematical models, machine learning prediction models, or economic evaluation models to assess the impact of the initial optimization objective on the full life-cycle operating cost of the energy storage battery based on the initial optimization objective and lifespan degradation characteristics. For example, firstly, based on the initial optimization objective, simulate the expected operating conditions of the energy storage battery over a future period (e.g., charge / discharge cycle count, average depth of charge / discharge, average charge / discharge rate, and operating temperature). Secondly, combine this with a lifespan degradation characteristic model of the energy storage battery (e.g., a capacity model based on cycle count and depth of charge / discharge). The degradation model, along with a calendar-based lifespan degradation model based on temperature and state of charge, predicts the capacity degradation rate and internal resistance increase rate of the energy storage battery under simulated operating conditions. Then, based on the predicted capacity degradation and internal resistance increase, the time required for the energy storage battery to reach its end-of-life criteria (e.g., capacity degradation to 80% of initial capacity) is estimated. Finally, factors such as the battery's initial purchase cost, maintenance costs during operation, expected replacement costs, and residual value are taken into consideration to calculate the total operating cost of the energy storage battery over its entire lifespan (impact assessment results) under the initial optimization objectives. Step S44 can balance the stability requirements of the microgrid with the long-term economics of the energy storage battery by modifying the focus of the initial optimization objective based on the impact assessment results. For example, if the impact assessment results show that a certain initial optimization objective will lead to a significant reduction in the lifespan of the energy storage battery, thereby greatly increasing the total life cycle operating cost, the focus of the initial optimization objective can be adjusted. For example, the dependence on the battery's fast response capability can be appropriately reduced, or the charging and discharging strategy can be adjusted to extend the battery life. Even if this may slightly increase other operating costs or reduce some stability margin in the short term, it can achieve overall optimization in the long term.

[0089] This solution achieves a balance between the economy and reliability of the scheduling strategy by considering not only the stability risk level of the microgrid but also the lifespan degradation characteristics of energy storage batteries and their impact on the total lifecycle operating cost when adjusting the focus of scheduling optimization objectives. Specifically, after initially determining the focus of scheduling optimization objectives, the impact of the energy storage batteries' lifespan degradation characteristics on the long-term health and economic costs of the batteries can be quantitatively assessed. This forward-looking cost assessment allows the scheduling system to identify scheduling strategies that may lead to premature battery retirement or excessively high operating costs, and to revise the initial optimization objectives accordingly. Therefore, the final scheduling optimization objectives not only effectively address the stability risks of the microgrid but also consider the long-term operational economics of the energy storage batteries, avoiding the problem of sacrificing long-term economic benefits for excessive pursuit of short-term stability. Through the aforementioned technical solution, this application effectively addresses the problem of excessively high life-cycle operating costs of energy storage batteries that might arise from considering only stability risks by incorporating the battery lifespan degradation characteristics into the scheduling optimization process. This approach ensures the stable operation of the microgrid, significantly extends the battery lifespan, and reduces battery replacement frequency and maintenance costs, thereby substantially reducing the overall operating cost of the microgrid. This comprehensive scheduling method, which considers both stability and economics, enhances the long-term sustainable operation and economic benefits of the microgrid, achieving smarter and more optimized energy management.

[0090] In some preferred embodiments, a specific example is given below. Suppose that at a certain operating moment, the microgrid's stability risk level is high, and it is initially determined that the energy storage battery needs to provide high-frequency, deep charge-discharge cycles to quickly respond to load fluctuations. Therefore, the initial optimization objective is adjusted to "prioritize ensuring system frequency stability." At this time, the system will acquire the current energy storage battery's lifespan degradation characteristics; for example, the battery's cycle life will be significantly shortened under frequent deep charge-discharge cycles. Based on this, the system will assess the expected lifespan loss of the energy storage battery over the next five years and the resulting replacement costs if scheduling is carried out according to the initial optimization objective of "prioritizing system frequency stability." If the assessment results show that this scheduling method will lead to the battery needing replacement within two years, and the replacement cost is far higher than the short-term operating costs that could be saved through other means (e.g., appropriately increasing diesel generator output or allowing slight frequency fluctuations), then the system will revise the initial optimization objective based on this impact assessment result. The revised final scheduling optimization objective may focus on "while meeting basic frequency stability requirements, taking into account the lifespan of energy storage batteries, appropriately adjusting charging and discharging strategies, and allowing diesel generators to undertake some rapid response tasks under specific circumstances." In this way, the scheduling system can effectively balance the long-term economic benefits of energy storage batteries while ensuring the stability of the microgrid, avoiding long-term cost increases caused by short-term decisions.

[0091] In some preferred embodiments, step S44 includes:

[0092] S441. Obtain fuel price information and battery type information for energy storage batteries;

[0093] S442. Determine the key correction strategy based on the impact assessment results, fuel price information, and battery type information;

[0094] S443. Use the emphasis correction strategy to correct the initial optimization target emphasis in order to obtain the final scheduling optimization target emphasis.

[0095] The fuel price information in step S441 can be understood as the fuel cost required for the diesel generator to operate. This information can be obtained based on real-time market prices, historical price trends, long-term purchase contract prices, or forecasting models. The purpose of obtaining fuel price information is to provide accurate economic input for the operating costs of the diesel generator, enabling a more precise balance between the economic benefits of diesel power generation and energy storage charging / discharging in dispatching decisions. The battery type information for energy storage batteries refers to key parameters such as the battery's chemical composition (e.g., lithium iron phosphate batteries, ternary lithium batteries, lead-acid batteries, etc.), rated capacity, maximum charge / discharge power, charge / discharge efficiency, cycle life characteristic curve, and temperature characteristics. The purpose of obtaining battery type information is to gain a deeper understanding of the energy storage battery's performance boundaries, degradation mechanisms, and response characteristics under different operating conditions. This allows for better management of batteries during dispatching, avoiding overuse or improper operation, extending their lifespan, and reducing long-term operating costs. The emphasis adjustment strategy in step S442 refers to formulating specific rules or algorithms to adjust the emphasis of the initial optimization target based on the impact assessment results, fuel price information, and battery type information. For example, when the fuel price is high, the strategy may tend to make more use of the energy storage battery discharge to reduce the operating time of the diesel generator; when the energy storage battery type shows that it has good tolerance to deep discharge, the strategy may allow a greater depth of discharge to provide stronger power support. The determination of this strategy can be based on a preset rule base, expert system, fuzzy logic, or machine learning model. Step S443 uses a weighting correction strategy to correct the initial optimization objective weighting to obtain the final scheduling optimization objective weighting. Specifically, the correction process can adopt methods such as weighted averaging, multi-objective optimization algorithms, constraint adjustment, or priority ranking. For example, fuel cost and battery life cost can be treated as two independent optimization objectives, and their weights in the overall optimization objective function can be adjusted through the weighting correction strategy. Alternatively, the operating constraints in the scheduling algorithm can be dynamically adjusted according to fuel price and battery type, thereby ensuring that the final scheduling optimization objective weighting can comprehensively consider multiple dimensions such as economy, stability, and equipment life, and achieve the long-term optimal operation of the microgrid.

[0096] This solution addresses the limitations of the aforementioned correction process, which failed to adequately consider external economic factors and the inherent characteristics of energy storage batteries, by incorporating fuel price information and battery type information when revising the focus of dispatch optimization. Specifically, fuel price information is used to precisely quantify the operating costs of diesel generators, enabling the dispatch system to more accurately assess the economic trade-off between diesel power generation and energy storage charging / discharging under different market conditions. Battery type information provides crucial data on battery performance, degradation patterns, and optimal operating ranges, allowing dispatch strategies to avoid excessive battery wear, thereby extending their lifespan and reducing long-term maintenance costs. The integration of this key information allows the revision of the initial optimization focus to expand from a single consideration of stability or preliminary economics to a more comprehensive optimization that incorporates dynamic economic factors and equipment characteristics, ensuring that the final dispatch optimization focus guides the microgrid towards more globally optimal operation. Through this technical solution, the process of revising the dispatch optimization focus becomes more refined and intelligent, effectively avoiding suboptimal dispatch decisions caused by fuel price fluctuations or differences in battery type, thus significantly improving the overall economic efficiency and long-term reliability of microgrid operation. Specifically, this solution ensures stable microgrid operation while minimizing fuel consumption costs by optimizing the coordinated operation of diesel generators and energy storage batteries. It also effectively manages battery degradation, extending battery lifespan and ultimately reducing total lifecycle operating costs. This allows microgrid dispatch strategies to not only respond to immediate operational needs but also consider long-term economic viability and sustainability.

[0097] In some preferred embodiments, a specific example is given below. Suppose that at a certain period, the initial optimization objective of the microgrid is set to prioritize ensuring system frequency stability, and the lifespan degradation characteristics of the energy storage battery are initially considered. However, during the correction phase, the system obtains current fuel price information showing that diesel prices are at historically high levels. Simultaneously, the battery type information indicates that it is a high-energy-density ternary lithium battery, which is sensitive to deep discharge and high-rate charge / discharge. In this situation, if corrections are made solely based on the initial lifespan degradation assessment, the battery may still be overused in pursuit of frequency stability and initial economic efficiency. According to the solution of this application, in step S441, the system obtains high fuel price information and ternary lithium battery type information. In step S442, based on this information, the correction strategy is determined as follows: while ensuring frequency stability, appropriately increase the output ratio of the diesel generator to reduce the depth of discharge and charge / discharge cycle number of the energy storage battery during periods of high fuel prices. Simultaneously, based on the characteristics of the ternary lithium battery, stricter limits are set on the charge / discharge power and depth to avoid accelerating battery degradation. In step S443, the initial optimization objective is modified using this emphasis correction strategy. For example, the modified optimization objective might increase the weight of fuel cost and tighten the constraints on battery health management (based on battery type). Thus, the final scheduling optimization objective will guide the system in the subsequent S5 step to generate more balanced energy storage battery charging and discharging commands, as well as diesel generator start / stop and output commands. For instance, during peak load periods, even with higher fuel prices, the diesel generator might be allowed to bear more load to protect the energy storage battery, thereby achieving better economy and reliability in long-term operation.

[0098] Secondly, such as Figure 3 As shown, this application also provides a smart dispatching system for a photovoltaic-storage-diesel microgrid, which includes:

[0099] Data acquisition module 1 is used to acquire energy storage battery operation data, diesel generator operation data and microgrid global operation data;

[0100] Capability assessment module 2 is used to assess the power response capability of the energy storage battery based on the energy storage battery operation data, and to assess the rapid response capability of the diesel generator based on the diesel generator operation data.

[0101] Risk assessment module 3 is used to analyze the stability risk level of the microgrid based on the microgrid's global operation data, power response capability, and rapid response capability.

[0102] The emphasis adjustment module 4 is used to adjust the emphasis of the scheduling optimization target according to the stability risk level;

[0103] Execution module 5 is used to obtain energy storage battery charging and discharging instructions, diesel generator start / stop instructions, and output instructions based on the adjusted scheduling optimization target, and execute all the obtained instructions.

[0104] This application provides a smart dispatching system for a photovoltaic-storage-diesel microgrid, comprising a data acquisition module 1, a capacity assessment module 2, a risk judgment module 3, a focus adjustment module 4, and an execution module 5. This embodiment of the smart dispatching system for a photovoltaic-storage-diesel microgrid is used to execute the steps in the smart dispatching method for a photovoltaic-storage-diesel microgrid provided in the first aspect above. The principle of the smart dispatching system for a photovoltaic-storage-diesel microgrid provided in this embodiment is the same as that of the smart dispatching method for a photovoltaic-storage-diesel microgrid provided in the first aspect above, and will not be discussed in detail here.

[0105] As can be seen from the above, the intelligent dispatching method and system for photovoltaic-storage-diesel microgrids provided in this application can first analyze the stability risk level of the microgrid based on the global operation data, power response capability, and rapid response capability of the microgrid, and then adjust the focus of dispatching optimization objectives according to the stability risk level. This allows the dispatching system to flexibly balance economic and reliability objectives based on the real-time health status and potential risks of the microgrid, thus avoiding the problem of sacrificing system stability due to blindly pursuing cost minimization in traditional strategies. Therefore, this application can significantly improve the overall operational reliability of the microgrid, extend the service life of energy storage batteries, and reduce economic losses caused by system instability.

[0106] In the embodiments provided in this application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of the above units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another robot, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0107] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0108] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0109] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A smart dispatching method for a photovoltaic-storage-diesel microgrid, characterized in that, The intelligent scheduling method for photovoltaic-storage-diesel microgrids includes the following steps: S1. Acquire energy storage battery operation data, diesel generator operation data, and microgrid global operation data; S2. Evaluate the power response capability of the energy storage battery based on the energy storage battery operation data, and evaluate the rapid response capability of the diesel generator based on the diesel generator operation data. S3. Analyze the stability risk level of the microgrid based on the global operation data of the microgrid, the power response capability, and the fast response capability; S4. Adjust the focus of scheduling optimization objectives according to the stability risk level; S5. Based on the adjusted scheduling optimization target, focus on obtaining the energy storage battery charging and discharging instructions, as well as the diesel generator start / stop instructions and output instructions, and execute all the obtained instructions.

2. The intelligent dispatching method for photovoltaic-storage-diesel microgrids according to claim 1, characterized in that, The energy storage battery operating data includes the battery's terminal voltage, charging and discharging current, state of charge, internal temperature, and output power. The diesel generator operating data includes the diesel generator's generator speed, coolant temperature, fuel pressure, and operating mode. The microgrid global operating data includes the bus voltage, bus frequency, and total load demand.

3. The intelligent dispatching method for photovoltaic-storage-diesel microgrids according to claim 1, characterized in that, Step S2 includes: S21. Based on the operating data of the energy storage battery, make a preliminary assessment of the power response capability of the energy storage battery to obtain a preliminary assessment result; S22. Evaluate the rapid response capability of the diesel generator based on the diesel generator operating data; S23. Based on the energy storage battery operation data, diesel generator operation data and microgrid global operation data during the load impact event, obtain the bus voltage fluctuation amplitude, bus frequency fluctuation amplitude, actual output of diesel generator and actual output power of battery. S24. Obtain the instantaneous output power of the energy storage battery based on the bus voltage fluctuation amplitude, the bus frequency fluctuation amplitude, the actual output of the diesel generator, and the electrical characteristics of the microgrid. S25. Based on the comparison between the instantaneous output power and the actual output power of the battery, the preliminary evaluation result is corrected to obtain the power response capability of the energy storage battery.

4. The intelligent dispatching method for photovoltaic-storage-diesel microgrids according to claim 3, characterized in that, Step S22 includes: S221. Obtain the ambient temperature, ambient humidity, and ambient altitude of the diesel generator location, and obtain the historical operating data and maintenance records of the diesel generator; S222. Adjust the preset start-up time characteristic curve and power ramp-up rate characteristic curve according to the ambient temperature, ambient humidity, ambient altitude, historical operating data and maintenance records; S223. Evaluate the rapid response capability of the diesel generator based on the adjusted start-up time characteristic curve, power ramp-up rate characteristic curve, and the diesel generator operating data.

5. The intelligent dispatching method for photovoltaic-storage-diesel microgrids according to claim 3, characterized in that, Step S24 includes: S241. Obtain the switching status and distributed load operating status in the microgrid when a load surge event occurs; S242. Determine the electrical characteristics of the microgrid based on the switch status and the operating status of the distributed load; S243. Obtain the instantaneous output power of the energy storage battery based on the bus voltage fluctuation amplitude, the bus frequency fluctuation amplitude, the actual output of the diesel generator, and the electrical characteristics of the microgrid.

6. The intelligent dispatching method for photovoltaic-storage-diesel microgrids according to claim 3, characterized in that, Step S25 includes: S251. Obtain the instantaneous reactive power change rate and voltage-current phase angle change rate based on the microgrid global operation data when a load impact event occurs. S252. The instantaneous output power is corrected based on the instantaneous reactive power change rate and the voltage-current phase angle change rate to reduce the impact of system response caused by non-energy storage battery performance factors on the instantaneous output power. S253. The preliminary evaluation result is corrected based on the comparison between the corrected instantaneous output power and the actual output power of the battery, so as to obtain the power response capability of the energy storage battery.

7. The intelligent dispatching method for photovoltaic-storage-diesel microgrids according to claim 1, characterized in that, Step S3 includes: S31. Obtain the microgrid's operating mode, load characteristic parameters, and power configuration information; S32. Determine the risk level assessment strategy based on the operating mode, the load characteristic parameters, and the power configuration information; S33. Based on the risk level judgment strategy, analyze the stability risk level of the microgrid according to the global operation data of the microgrid, the power response capability, and the fast response capability.

8. The intelligent dispatching method for photovoltaic-storage-diesel microgrids according to claim 1, characterized in that, Step S4 includes: S41. Adjust the focus of the scheduling optimization objective according to the stability risk level to obtain the preliminary optimization objective focus; S42. Obtain the lifespan degradation characteristics of the energy storage battery; S43. Evaluate the impact of the initial optimization objective on the full life cycle operating cost of the energy storage battery based on the initial optimization objective focus and the life decay characteristics, so as to obtain the impact evaluation results; S44. Based on the impact assessment results, the initial optimization objective is revised to obtain the final scheduling optimization objective.

9. The intelligent dispatching method for photovoltaic-storage-diesel microgrids according to claim 8, characterized in that, Step S44 includes: S441. Obtain fuel price information and battery type information of the energy storage battery; S442. Determine the focus correction strategy based on the impact assessment results, the fuel price information, and the battery type information; S443. The initial optimization target emphasis is modified using the aforementioned emphasis correction strategy to obtain the final scheduling optimization target emphasis.

10. A smart dispatching system for a photovoltaic-storage-diesel microgrid, characterized in that, The intelligent dispatching system for the photovoltaic-storage-diesel microgrid includes: The data acquisition module is used to acquire operating data of energy storage batteries, operating data of diesel generators, and global operating data of the microgrid. The capability assessment module is used to assess the power response capability of the energy storage battery based on the energy storage battery's operating data, and to assess the rapid response capability of the diesel generator based on the diesel generator's operating data. The risk assessment module is used to analyze the stability risk level of the microgrid based on the global operation data of the microgrid, the power response capability, and the fast response capability. The emphasis adjustment module is used to adjust the emphasis of scheduling optimization objectives based on the stability risk level. The execution module is used to obtain energy storage battery charging and discharging instructions, diesel generator start / stop instructions, and output instructions based on the adjusted scheduling optimization objectives, and execute all the obtained instructions.

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